Introduction 

Cereals have played an essential role in the development of human civilization and have been cultivated for nearly ten thousand years.  By definition, a cereal is any cultivated grass grown for the edible components of its grain and can also refer to the resulting grain itself.  The edible components of cereal grain are the endosperm, germ, and bran.  Unprocessed grains have high nutritional content and are a rich source of vitamins, minerals, carbohydrates, fats, oils, and protein.  The bran and germ are often removed in processed cereal products and the endosperm is composed of mostly carbohydrates.  Cereal grains can be used for human food, animal feed, biodiesel, and as a starch source for conversion into fermentable sugars.  Cereals are grown in greater quantities and provide more food energy worldwide than any other type of crop.  The long shelf life, high caloric content, and nutritional composition of grains makes them an ideal food source, especially in impoverished and developing countries.  The most widely produced cereal crops worldwide are maize, wheat, rice, barley, and sorghum.  Many types of cereals are adaptable to different climates and growing conditions and others are specific to certain parts of the world.  Barley in particular is known for being versatile and adaptable to unfavorable climate and soil conditions.  It is a top source of animal feed for cattle and has superior properties for malting and brewing.  Barley flour is used in many types of foods, such as stews, soups, pastas, noodles, sauces, and baked products.  One important processed product made from cereal grains is breakfast cereals, which have gained popularity as a ready-to-eat food that is being repositioned as not only a breakfast food but as a snack or dessert in cereal bar food.  Companies are constantly marketing new and eye-catching flavors along with playing to increased consumer awareness of health and nutritional benefits of foods.  There are many different types of breakfast cereals and quality control during manufacturing is very important to meet proper specifications.  With demand continuing to grow and research moving forward at a rapid pace, there is a need for new testing methods to meet the challenges of optimizing cereal grain breeding, growing, harvesting, and processing as well as manufacturing of different cereal based products.  Traditional methods are often expensive, time-consuming, and impractical for use on a large scale.  One method which has shown potential for measuring parameters of interest in cereals that is fast, non-invasive, and able to be implemented for large-scale testing is NIR spectroscopy. 

Analytes 

• Starch 
• Amylose 
• Sugars 
• Protein 
• Waxy wheat discrimination analysis 
• Viscosity parameters 
• Degree of gelatinization 
• Dry matter 
• Nitrogen 
• Hardness 
• Soluble sugars 
• Oil 
• Moisture 
• Toxin levels 
• Crude protein 
• Lipids 
• Endosperm texture Superoxide Dismutase (SOD) Activity 
• Total Amino Acids (TAA) 
• Fusarium infection 
• Selection of optimal high yield and high quality malt barley varieties 
• Optimization of processing conditions in barley milk manufacturing 
• Glucose 
• Fructose 
• Sucrose 
• Total sugars 
• Classification of breakfast cereal bars 

A Review on the Role of Vibrational Spectroscopy as an Analytical Method to Measure Biochemical and Biophysical Properties in Cereals and Starchy Foods 

Starch is the major component of cereal grains and other starchy foods.  Stored starch in the seeds and tubers of many agricultural crops provides the main source of energy in human diets, including cereal crops like maize, wheat, rice, barley, and sorghum.  Changes in the biochemical and biophysical properties of starch are directly related to the ratio of amylose and amylopectin.  Amylose is a polysaccharide that makes up about 20% to 30% of total starch in most starch containing plants.  It has a tightly packed helical structure, making it more resistant to digestion than other starch molecules and an important component of resistant starch.  Amylopectin makes up 70% to 80% in most starch containing plants, although it varies depending on the source.  It is a water soluble polysaccharide bearing a linear chain with linked glucose units that can branch into side chains.  Amylopectin is higher in medium-grain rice, waxy potato starch, and waxy corn, can be up to 100% in glutinous rice, and is lower in long-grain rice and amylomaize.  The ratio of the two starch components influences properties like viscosity and gelatinization that affect the end use of the compound.  Current methods for measuring chemical and physical properties of starch are slow, require sample preparation and destruction, and are often impractical for large-scale use.  Two such methods are Differential Scanning Calorimetry (DSC) and Rapid Visco Analyzer (RVA).  One example of RVA use in cereal analysis is to determine the effects of rain damage on grain quality at the point of delivery.  Over the last twenty-five to thirty years, vibrational spectroscopy and chemometric techniques have been examined for developing rapid methods for determining biochemical and biophysical properties of interest in cereals and other starchy foods.  NIR spectroscopy is particularly well-suited for measuring starch-related parameters, offering the advantages of fast analysis, no sample destruction, minimal or no sample preparation, and the ability to measure multiple parameters from one light scan.  According to one report, NIR spectroscopy is currently applied in three different ways in cereal and starchy foods analysis: straightforward and rapid determination of composition, a screening tool in plant breeding, and an in-line tool to monitor chemical and physical changes during processing.  This review paper examines various applications for measuring and monitoring both biochemical composition (amylose, amylopectin, and starch) and biophysical properties (pasting properties, viscosity) in cereals and starchy foods. 

Amylose, Amylopectin, Starch, and Granule Structure 

Parameter Sample Statistics 

Starch Buckwheat Flour	R² = 0.93
Starch Sweet Potato	R² = 0.95	SEP = 1.91%
Starch-Amylose (SAC) Corn	R² = 0.96	SEP = 5.1%
Starch Potato Tubers	R² = 0.90	SECV = 0.74%
Amylose Sorghum	R² = 0.75	SEP = 0.77%
Starch Yam Tubers	R² = 0.84
Sugars Yam Tubers	R² = 0.86
Proteins Yam Tubers	R² = 0.88
Starch Taro	R² = 0.89
Sugars Taro	R² = 0.90
Amylose Rice		SEP = 0.31%
Amylose Beans		SEP = 12.8 g/kg
Amylose Barley		SEP = 1.09%
Starch Barley		SEP = 0.98
Amylose Yam		SEP = 3.71%
Starch Yam		SEP = 1.78%
Crude Starch Maize		SEP = 0.96%

Waxy Wheat vs. Partial Waxy Discriminant Analysis – Accuracy greater than 90% 

The statistics shown above are from various application studies examining the feasibility of measuring biochemical parameters in starch-based foods.  High correlation coefficients and low SEP measurements were found for starch in many of the samples, with a correlation coefficient greater than 0.90 for buckwheat flour, sweet potato, corn, and potato tubers. In the case of sweet potatoes, good predictions were obtained from samples from the same harvests but the model could not account for variances in samples from other harvests or years, indicating that further calibration work is necessary before using the calibration model in a practical setting.   Slight lower correlation and reasonable SEP numbers were found for yam, yam tubers, taro, and barley as well as for crude starch in maize.  Application studies for amylose showed good correlation as well, especially in the case of starch-amylose content (SAC) in corn.  This particular study used a set of genotypes with endosperm mutations, creating a range from 8.5% to 76% in SAC.  This large range likely contributed to the good results.  Amylose in sorghum was determined using both whole and ground samples, with better results coming from the ground samples.  This is expected when measuring a biochemical property as ground samples are more homogenous.  Good results were also obtained measuring sugars, proteins, and starch in yam tubers.  In the case of the discriminant analysis study for wheat, waxy wheat that was developed free of amylose was used along with partial waxy and wild genotypes.  The Linear Discriminant Analysis (LDA) model was able to discriminate the waxy wheat with an accuracy over 90%, although results were less accurate for the other two types.  The authors of the study suggested that the spectral sensitivity to waxiness diminishes with reduction of the lipid-amylose complex that is lowered as waxiness decreases.  Overall, these studies show the feasibility of using NIR spectroscopy and chemometric models for determining biochemical parameters of interest in cereals and other starch-based foods. 

Gelatinization, Pasting Properties, and Retrogradation of Starch 

PV	Peak Viscosity
BD	Breakdown
SB	Setback
HPV	Hot Pasting Viscosity
TH	Though
FV	Final Viscosity
RVU	Rapid Visco Units

Parameter (RVU) Sample Statistics 

BD Rice	R² = 0.88	SEP = 10.2
SB Rice	R² = 0.92	SEP = 13.6
PV Rice	R² = 0.74	SEP = 20.99
BD Rice	R² = 0.80	SEP = 21.47
SB Rice	R² = 0.97	SEP = 22.23
TH Rice	R² = 0.80	SEP = 7.37
FV Rice	R² = 0.95	SEP = 13.2
PV Sweet Potato	R² = 0.91	SEP = 13.1
BD Sweet Potato	R² = 0.81	SEP = 10.67
SB Sweet Potato	R² = 0.92	SEP = 1.82
PV Maize	R² = 0.92	SEP = 183
BD Maize	R² = 0.92	SEP = 232
SB Maize	R² = 0.92	SEP = 412
Degree of Gelatinization Pasta	R² = 0.97	SEP = 0.24

RVA instruments are widely used in assessing cooking and processing characteristics in food, especially in rice.  One study examined NIR spectral changes due to changes in structure of starch from gelatinization. Numerous wavelengths showed notable changes in the second derivative spectra, especially in the wavelength range from 2100 nm to 2280 nm.  The authors speculated that effects on particle size explained the changes in the NIR spectra, as high correlation exists between particle size and degree of gelatinization.  Numerous studies have shown that NIR spectroscopy and calibration models can be used to predict biophysical viscosity parameters with good accuracy as is shown in the statistics listed above.  The variability of results in these studies can be attributed to a number of factors, such as the accuracy of the reference method, interferences with other properties, range of values in the parameters of interest, number of samples used, and sources of variability in natural products.  It is often the case that differences in climate, soil composition, harvest year, breed, genotype, and variety in agricultural products can create differences in NIR spectra that are not directly attributed to changes in the parameters of interest.  Calibration models must contain samples that cover all these sources of variability in order to make accurate predictions and the process of making such a model is called creating a “robust” model.  There are challenges in the interpretation of complex data using multivariate methods and calibration development, but the advantages of using NIR spectroscopy as a fast, non-invasive, and cost-effective method to predict parameters of interest in cereals and starchy foods ensures that continued research and development will occur. 

A Review on the Role of Vibrational Spectroscopy as An Analytical Method to Measure Starch Biochemical and Biophysical Properties in Cereals and Starchy Foods – PubMed (nih.gov)  

An Overview on the Use of Infrared Sensors for in Field, Proximal, and at Harvest Monitoring of Cereal Crops 

There is a demand from farmers for rapid, cost-effective, green, and non-destructive methods for monitoring changes in the physical and chemical properties of crops.  Monitoring properties throughout the lifecycle of the plant can help establish the optimum harvest date, improve agronomic management practices, and improve crop diagnostics.  The concepts of water and nitrogen use and efficiency have been around for a long time but their use is minimal as part of the decision making process and are often not used as metrics for evaluating farm performance due to a lack of adequate tools and sensors.  Farmers, researchers, and instrument manufacturers are constantly looking for ways to develop new sensors that can evaluate efficiency to improve production and processes.  Proximal sensors can provide a powerful tool for analysis of soil physical properties, chemical properties, and crop diseases.  One potential tool for monitoring cereal and agricultural crops is NIR spectroscopy.  NIR spectroscopy provides the advantages of being fast, non-invasive, no sample destruction, little or no sample preparation, requires no toxic chemicals or solvents, and the ability for large-scale monitoring as well as being able to measure multiple parameters of interest with a single light scan.  Research and development is happening with a number of applications and the use of NIR spectroscopy as a practical tool is dependent on multiple factors, such as instrument cost and availability, using the instrument in the field or on-line, and model robustness, accuracy, and precision.  This review paper examines application studies using NIR spectroscopy to monitor dry matter (DM), yield, nitrogen, and pest and diseases in various cereal crops. 

Dry matter is one of the most important parameters in crop production as it is directly related to production costs.  The significance of determining water status in plants has been increased in the context of climate change and scheduling irrigation times and volumes, preserving water, and manipulating composition are of utmost importance.  Water is a proven parameter that can be measured using NIR spectroscopy as water is very absorbing of NIR light in the wavelength range above 1000 nm and even small changes create marked differences in the NIR spectra.  However, there are logistical and technical challenges in making measurements of plants or crops on a farm.  These include the creation of robust calibration models that cover variability in NIR spectra caused by differences in soil, breed, variety, genotypes, and other sources of variability in agricultural products.  In recent years, the development of portable field instruments has facilitated the direct measurement of samples in the field.  Such analysis is advantageous for monitoring fresh plant samples without the need for drying, grinding, or sending the sample to the laboratory.  Numerous authors have reported that one of the major causes for low nitrogen use efficiency is the poor synchrony between soil nitrogen supply and crop demand.  Traditional reference methods for determining nitrogen concentration are the Kjeldahl and Dumas methods which are accurate but are also time-consuming, expensive, require the use of chemicals and solvents, and are ill-suited for widespread testing.  Studies in recent years have demonstrated the potential of NIR spectroscopy in determining nitrogen concentration in grass samples and as a replacement for wet chemistry methods with online field screening, helping to facilitate improved nitrogen uptake efficiency and total concentration.   

The feasibility of using NIR spectroscopy has been evaluated for multiple harvest applications in cereal crops.  Cereal grains can be analyzed whole, as ground powder, or in some cases as single seeds when determining different chemical properties.  Classification of maize kernels based on starch composition, hardness, and toxin levels has been examined in application studies.  Likewise, dry matter, starch, soluble sugars and crude protein in several types of cereal grains have been studied using NIR spectroscopy and these four parameters have all shown good results in studies.  In the case of single seeds, best results have been obtained from plants with small seeds and a relatively uniform distribution of seed constituents, such as rapeseed, wheat, sunflower, soybean, and cottonseed.  Oil and protein are two examples of parameters in single seeds that can be measured using NIR spectroscopy.  Spectroscopic techniques have also been examined for detection of both symptomatic and asymptomatic plant diseases as well as pest infestation.  One study examined the percentage of Aspergillus fungal infection in rice samples and another identified aflatoxin B1 in paddy rice samples.  Other studies examined deoxynivalenol and other mycotoxins in various types of cereals.  It must be noted that the concentration of these toxins in plants is often far below the threshold of detection for NIR spectroscopy.  It is likely that calibration models are actually correlating to a measurable parameter that is being affected directly by the toxic contamination.  While such an indirect correlation is acceptable, such models must be examined carefully when building the calibration to ensure the models are valid.  Overall, these studies have shown the important role and potential of NIR spectroscopy in cereal crop analysis.  It can optimize manpower and expenditure required for crop analysis, reduce sampling error, and deliver more representative measurements of plots on a farm.  Farmers in Australia, Canada, Europe, and the United States are using NIR spectroscopy to determine parameters like protein and dry matter during harvest.  Research and development continue and the potential savings, quick analysis time, and environmentally friendly nature of using NIR spectroscopy could one day lead to the application of NIR spectroscopy across the entire food supply chain.   

Agriculture | Free Full-Text | An Overview on the Use of Infrared Sensors for in Field, Proximal and at Harvest Monitoring of Cereal Crops (mdpi.com) 

Development of NIRS Equations for Food Grain Quality Traits Through Exploitation of a Core Collection of Cultivated Sorghum 

Sorghum is a major food cereal in Asian and African countries.  Some examples of traditional foods made with sorghum include porridge in western Africa, ugali in Eastern Africa, couscous, masa, and tortillas.  Grain quality is determined by biochemical and physical parameters that influence rheological and sensory properties of sorghum dishes.  For example, the consistency of thick porridge is significantly correlated with amylose content but negatively correlated with protein and lipid content.  Cooked couscous firmness correlates positively with amylose while waxy sorghums that contain little or no amylose produce sticky masa and tortillas with poor rollability.  Endosperm texture and hardness affect grain mold resistance, grain storage ability, milling behavior, flour particle size, and cooking properties.  Hard grains produce flours that have a high proportion of coarse particles with low ash content.  These grains yield a high proportion of desirable sorghum couscous granules.  Standard laboratory methods for measuring these parameters are time-consuming, expensive, often require the use of toxic chemicals and solvents, and are impractical for measuring samples on a large scale.  NIR spectroscopy was examined for determining quality traits in sorghum.  It offers the advantages of being fast, non-invasive, no sample destruction, little or no sample preparation, and the ability to measure multiple parameters with a single light scan.  The objective of the study was to use a large and diverse sorghum core collection to develop NIR calibration models for amylose, protein, lipids, endosperm texture, and hardness for the purpose of varietal comparisons and genetic analyses in the framework of a breeding program.  Two hundred and five accessions of the core collection were analyzed from five basic races and five intermediate races.  The accessions originated from thirty-nine different countries.  Most samples were harvested from an irrigated trial conducted during a single dry season in Senegal.  Eight local varieties were also added to the study and in total, two hundred and seventy-eight samples were procured for the study.  All grains were cleaned, sifted through a sieve adapted to the average grain size of each sample, and moisture content was measured to calculate the quantity of water needed to reach 11.5% moisture.  Water was added and the samples were stored in sealed containers for a minimum of eight days before use.  20 g of each sample was ground in a 0.8 mm sieve.  For each sample, both whole grains and ground portion were scanned using an NIR spectrometer from 400 nm to 2500 nm at 2 nm intervals.  Thirty-two scans were collected per reading and averaged into a single spectrum.  Whole grain samples were collected in duplicate while only one spectrum was collected for ground samples.  Traditional reference methods were used on the samples to determine values for the parameters of interest.  Various pre-processing methods were performed on the spectral data before chemometric analysis.  Principle Component Analysis (PCA) was performed to determine variations in the NIR spectra and for outlier analysis.  Partial Least Squares (PLS) calibration models were created using the NIR spectra and reference values of the parameters of interest.   

Ground Sorghum Calibration Models 

Amylose	R² = 0.75
Protein	R² = 0.98
Lipids	R² = 0.91
Endosperm Texture	R² = 0.88
Hardness	R² = 0.90

Whole Grain Sorghum Calibration Models 

Amylose	R² = 0.70
Protein	R² = 0.95
Lipids	R² = 0.84
Endosperm Texture	R² = 0.85
Hardness	R² = 0.88

Results for the calibration models using the NIR spectra of both ground and whole grains showed good correlation for all parameters with the exception of amylose, which likely occurred because of a small range of values in the samples.  Results would likely improve by extending the range of values for amylose by using waxy and non-waxy grains as well as using progenies obtained by crossing contrasted varieties.  Previous studies using other cereals that compared ground grain sample vs. whole grain sample calibrations showed comparable results. The reasons for this include homogeneity of the ground samples and scattering effects on the spectra of whole grain samples due to differences in particle size.  This study proved the feasibility of using NIR spectroscopy and calibration models to determine quality traits in both whole and ground sorghum grains.   

Development of NIRS Equations for Food Grain Quality Traits through Exploitation of a Core Collection of Cultivated Sorghum | Journal of Agricultural and Food Chemistry (acs.org) 

Fast Analysis of Superoxide Dismutase (SOD) Activity in Barley Leaves Using Visible and Near Infrared Spectroscopy 

Barley is widely cultivated around the world, especially in Asia and Northern Africa.  It is considered one of the most adaptable cereal grain species and can be produced at higher altitudes and latitudes as well as further into the desert than any other cereal crop.  Oxidative stress is one of the detrimental effects of reduced oxygen and is an important phenomenon in many biological systems.  Superoxide dismutase (SOD) is one of the protective enzymes that plays an important role in protection against environmental adversity.  It can remove free radicals and improve stress tolerance.  The traditional method for determining SOD activity is to measure its ability to inhibit the photochemical reduction of nitroblue tetrazolium, which requires the use of toxic chemical reagents and sample destruction.  NIR spectroscopy was examined for determining the feasibility of predicting SOD activity in barley leaves.  Samples were procured from a farm in China.  A herbicide was used as a stressor with five different concentrations (0, 50, 100, 500, and 1000 mg/L) applied at the two leaf stage.  Seventy-five sample were collected during the growing period at intervals of five, ten, and fifteen days after herbicide treatment.  NIR spectra of the barley leaves were collected from 325 nm to 1075 nm at 1.5 nm intervals.  Three spectra were collected per sample and averaged into one spectrum.  All leaf samples were tested for SOD activity by the standard reference method.  Various preprocessing methods were applied to the spectral data before chemometric analysis.  Four separate regression algorithms were used to correlate the NIR spectra to SOD activity: Partial Least Squares (PLS), Multiple Linear Regression (MLR), Least Squares-Support Vector Machine (LS-SVM), and Gaussian Process (GP).  Fifty samples were used as a calibration set to create the regression models and the remaining twenty-five samples were used as an independent validation set.   

LS-SVM

R² = 0.9064

RMSEP = 0.5536 U/mg Pro

The first PLS model used the entire spectral range for the calibration and obtained decent results with pre-processed spectra.  The regression coefficients from the PLS model were then used to select thirty effective wavelengths as input for the LS-SVM model.  The best results were obtained from this model and both the LS-SVM and SP models showed better results than PLS and MLR.  LS-SVM and SP are both non-linear calibration algorithms and proved to be more suitable for determining SOD.  Independent predictions from the validation set proved the validity of the model.  While further study and more samples would be necessary before using this model in a practical setting, this study showed the potential and feasibility of using NIR spectroscopy to determine SOD activity in barley leaves. 

Sensors | Free Full-Text | Fast Analysis of Superoxide Dismutase (SOD) Activity in Barley Leaves Using Visible and Near Infrared Spectroscopy | HTML (mdpi.com) 

Quantitative Analysis of Total Amino Acid in Barley Leaves Under Herbicide Stress Using Spectroscopic Technology and Chemometrics 

Barley is one of the earliest cultivated cereal grains and is attracting renewed interest for its use as food and as a bioethanol feedstock.  It is known for drought resistance and the ability to mature in climates with a short growing season.  Amino acid content is an important physiological indicator of environmental stress during plant growing season.  A recently developed herbicide, ZJ0273, has been applied to remove and control weeds in barley fields (the same herbicide used in the above SOD leaves study).  It is an ALS (acetolactate synthase) inhibiting herbicide which affects the formation of branch chain amino acids like aspartic acid, valine, and proline.  Total amino acids (TAA) is an important parameter for understanding the effects of herbicides on barley growth.  The traditional method for measuring TAA is an automatic amino acid analyzer which is expensive, time-consuming, requires sample destruction, and is impractical for measuring large numbers of samples.  NIR spectroscopy was examined for the purpose of determining TAA in barley leaves, offering a fast, non-destructive method for helping to measure the effects of herbicide injury on barley plants.  Seventy-five barley leaf samples were procured from a farm in China for the study.  ZJ0273 was applied during the seeding stage at concentrations of 0, 50, 100, 500, and 1000 mg/L.  All samples were scanned using an NIR spectrometer from 325 nm to 1075 nm at 1.5 nm intervals.  Thirty scans were collected per reading and averaged into one spectrum.  Three separate spectra were collected per sample and further averaged into one spectrum.  Various pre-processing methods were applied to the spectral data before chemometric analysis.  Two separate regression algorithms were used to correlate the NIR spectra to TAA: Partial Least Squares (PLS) and Least Squares-Support Vector Machine (LS-SVM).  PLS is a bilinear modeling method while LS-SVM can be used for both linear and non-linear relationships between variables.  Fifty samples were used as a calibration set for both models while the remaining twenty-five samples were used as an independent validation set for predictions. 

PLS	R² = 0.935	RMSEP = 0.558
LS-SVM	R² = 0.936	RMSEP = 0.309

Results were successful using both calibration methods.  The first PLS model was created using the full spectral range and from the regression coefficients, significant wavelengths and latent variable were chosen as the inputs for the PLS and LS-SVM models shown above.  While the sample set was limited, the results showed the feasibility of using NIR spectroscopy to measure TAA in barley leaves.  Further study would be warranted before using these models in a practical setting and more variability needs to be incorporated into the calibration models, such as leaf samples at different growth stages and more varieties of barley. 

Quantitative analysis of total amino acid in barley leaves under herbicide stress using spectroscopic technology and chemometrics – PubMed (nih.gov) 

Classification of Fusarium Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis 

Fusarium is a pathogen that can grow in the heads of cereal crops such as barley and wheat that can decrease yield and degrade grain quality, resulting in enormous economic losses to farmers.  It can grow rapidly at temperatures between 10°C and 25°C in a high humidity environment after heavy rainfall.  The pathogen can winter in seeds, straw, stubbles, and soil after harvest, leading to formation of molds that can damage ears with a brown discoloration at the early stage and gradually covering them with red conidiospores.  In 1998 in Korea, fusarium infection damaged nearly forty thousand hectares of fields, which corresponds to 47.8% of the total cultivation area in the country.  It can lead to the production of mycotoxins like deoxynivalenol, nivalenol, and zearalenone, which can cause intoxication of livestock and major diseases in humans if consumed, particularly as a carcinogen.  Traditional methods for inspecting barley for fusarium and mycotoxin contamination include high performance liquid chromatography (HPLC), gas chromatography (GC), and enzyme-linked immunosorbent assay (ELISA).  While effective, these methods are time-consuming, expensive, require the use of toxic chemicals and solvents, and are impractical for measuring large amounts of samples.  NIR spectroscopy was examined for discriminating between fusarium infected barley and normal hulled barley.  It offers the advantages of being fast and non-invasive with no sample destruction and little or no sample preparation.  Five hundred and fifteen kernels of hulled barley were collected from five different Korean provinces for the study.  The samples were divided into a control group and experimental group.  One hundred and twenty-seven samples from a single province were not infected with fusarium.  The remaining samples were infected and came from four separate groups.  All samples were scanned using an NIR spectrometer from 1175 nm to 2170 nm.  Each sample was scanned three times each on the front of the barley which contains a crease and the back which has no crease.  The three spectra for each side were averaged for a total of two spectra per sample.  After collection of NIR spectra, samples underwent a culture experiment to determine if a fusarium infection was present and to verify if the classification of the samples was correct.  Various pre-processing methods were applied to the NIR spectra before chemometric analysis.  A Partial Least Squares-Discriminant Analysis (PLS-DA) model was used to discriminate between the fusarium infected and normal barley.  A PLS-DA model uses arbitrary values of zero and one to classify between two groups.  The model predicts a number from the NIR spectrum of the sample and uses that number to classify it.  Two separate models were created for the crease side of the hulled barley and the side without a crease.   

PLS-DA with crease	R² = 0.948	SEP – 0.105	Accuracy 99.66%
PLS-DA without crease	R² = 0.939	SEP – 0.113	Accuracy 99.21%

The results for both models were excellent and proved the feasibility of the discrimination analysis.  Correlation coefficients were high and SEP were low for both models.  Independent validation predictions showed a very high accuracy.  This study showed that NIR spectroscopy has the potential to be used as a fast, non-invasive method for classifying hulled barley based on fusarium infection.  

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis. – Abstract – Europe PMC 

Performance Evaluation of Malt Barley (Hordeum vulgare L.) Varieties for Yield and Quality Traits in Eastern Amhara Regional State, Ethiopia 

Archaeological evidence indicates that barley was first cultivated about ten thousand years ago in the Fertile Crescent and it continues to be an important feed, malt, and food crop in many countries all around the world.  In Ethiopia, barley is grown in a wide range of environments at altitudes ranging from fifteen hundred to thirty-five hundred meters above sea level.  The ratio of malt barley produced to food barley produced is quite small, despite favorable growing conditions and market demand for malt barley from domestic brewers.  One brewery imported over fifteen thousand tons of malting barley in a single year.  While production is increasing, certain regions and varieties produce more malt barley per hectare than others.  Variance in production can be due to many factors, such as low yielding varieties, low and unevenly distributed rainfall, poor agronomic intercrop practices, lack of crop rotation, and disease and pest problems.  There are important quality characteristics in barley such as kernel size, kernel protein content, malt extract, and diastatic power.  Protein content between 9% and 12.5% is typically acceptable for brewers.  If protein is too high, the malt has low extract yield and low protein level barley lacks the enzymes necessary to modify the barley kernel and to break down starch.  Different genotypes vary in these characteristics and they are also influenced by environmental factors.  Some genotypes may perform well in a particular environment but poorly in others. 

There is one particular area of Ethiopia where malt varieties have not been evaluated for yield.  It is an important area for barley farmers and especially for malt barley as a large local brewery is located nearby, resulting in increased demand for the product.  A study was conducted to identify a high yield, early matured, and high quality malt barley variety in this area.  NIR spectroscopy was used with other testing methods to identify a variety that optimizes the quality and quantity standards of the malt barley.  Eight separate malt barley varieties were planted during the first week of July with three replications in each of different locations.  Standard crop management practices were applied throughout the growing stage and plants were harvested in October.  This process was conducted over two different years and growing seasons as well.  After harvesting, cleaning, and threshing, one thousand kernel weight was determined as well as other plot-based and plant-based data.  Grain quality data was determined using a NIR spectrometer and calibration models for protein, starch, and moisture.  This data was used to determine grain yield and the moisture values were used to adjust the grain yield numbers accordingly.  While all varieties showed acceptable numbers within the industry standards for one thousand kernel weight, protein, and moisture, three particular varieties were shown to be high yield genotypes and two were shown to be low yield genotypes.  In this study, NIR spectroscopy was used as an important tool assisting in the selection of three high yield, optimal kernel size, and good protein content malt barley varieties in an area of Ethiopia where demand for this product is high.  Farmers can use this information to improve yield and quality of their products, resulting in improved production.  

Performance Evaluation of Malt Barley (Hordeum vulgare L.) Varieties for Yield and Quality Traits in Eastern Amhara Regional State, Ethiopia (hindawi.com) 

Classification and Processing Optimization of Barley Milk Production Using NIR Spectroscopy, Particle Size, and Total Dissolved Solids Analysis 

Barley is a grain with significant nutritional benefits as it is a very good source of dietary fiber, minerals, vitamins, phenolic acids, and phytic acids.  It has become used more often for milk production as a replacement for cow milk as many consumers are looking for an alternative to traditional dairy products.  The demand results from medical reasons such as lactose intolerance and cow milk allergy and a lifestyle choice as there is an increased demand for plant-based milk products with no cholesterol.  Plant based milk substitutes are manufactured by extracting the plant material in water, removing the solids, product formulation, homogenization, and heat treatment.  The resulting products are suspensions which contain plant materials and oils.  Research has shown that phase separation, stability, and quality of emulsions in milk and milk products can be successfully measured and characterized using NIR spectroscopy.  In this study, NIR spectroscopy was used with other testing methods to determine the optimal processing conditions for barley milk production and classification of finished barley milk.  Barley that was produced, harvested, processed, and packed was obtained from a local market for the study.  60 g of the barley was soaked in 90 mL of water for twelve hours.  An additional 135 mL of water was added to the soaked barley and blended for 15, 30, 45, and 60 seconds in a blender.  The barley milk was then filtered and separated from the spent barley grain.  Samples of the barley milk and spent barley grain for each blending time were stored at 4°C until analysis.  NIR spectra of both the barley milk and spent barley grain were collected from 904 nm to 1699 nm.  Three spectra were collected per sample and averaged into one spectrum.  After collection of absorbance spectra, all spectra were processed into first and second derivative.  Further tests were conducted for particle size distribution using a laser diffraction method, electrical conductivity, total dissolved solids, and light microscopy to identify particle types and structures present in the samples.  The purpose of NIR spectra collection and the subsequent tests was to see if differences in the NIR spectra between the samples with different blending times were clear enough to show that collection of NIR spectra can be used to choose the optimal blending time instead of performing the subsequent tests.  Principle Component Analysis (PCA) was first performed to investigate differences in the NIR spectra.  Spectra of the barley milk and barley spent grain were clearly separated into two groups and within those groups, the separation between the four blending times was much clearer for the milk than the spent grain.  Analysis of particle size distribution values showed little change in particle size diameter (about 25 µm) between samples blended for 45 and 60 seconds.  Likewise, there was marked change in electrical conductivity and total dissolved solids from 15 seconds of blending time to 45 seconds but little additional change for 60 seconds.  Based on these results, 45 seconds was shown as the optimal blending time for milk and the NIR spectra could be used as an alternative to the other tests because the spectral differences are marked enough to mark the blending time stop point.  The benefits of using this information could be enormous.  NIR spectroscopy offers the advantages of being fast and non-invasive as well as the ability to provide real-time measurements using optic fibers and a probe.  Determining the optimal blending end point quickly in barley milk manufacturing can result in vast amounts of savings in time, energy, and manpower.   

Classification and Processing Optimization of Barley Milk Production Using NIR Spectroscopy, Particle Size, and Total Dissolved Solids Analysis (hindawi.com) 

In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer 

Breakfast cereals are widely consumed worldwide because of their easy preparation, nutritional value, and assorted varieties and flavors.  Some breakfast cereals are a good source of micronutrients, such as folic acid, vitamin C, iron, zinc, fibers, and antioxidants.  However, large amounts of sugar are added to some breakfast cereals which can increase risk of obesity and diabetes as well as reduce the overall nutritional quality.  Per FDA regulations, the difference between laboratory analysis of sugars in cereals and the amount declared on the nutrition label must be +/- 20%.  Reports indicate that many store bought cereals have a significantly higher or lower sugar content than the label indicates.  It is important to monitor and control sugar content of breakfast cereals at every step of the manufacturing process as well as in the final product.  Traditional methods for measuring sugar in breakfast cereals often use chromatography or electrophoresis and are expensive, time-consuming, require skilled labor, can use toxic solvents and reagents, and are impractical for monitoring large numbers of samples.  NIR spectroscopy was examined for the purpose of determining sucrose, glucose, fructose, and total sugars in breakfast cereals.  The particular FT-NIR spectrometer used in this study was a novel prototype instrument with handheld capability and Bluetooth communication with a tablet.  One hundred and sixty-four cereal samples were procured for the study.  A snack manufacturer provided one hundred and four samples of sucrose coated cereal and the remaining sixty samples were commercial cereals purchased at grocery stores.  Samples were ground in a blender to obtain even particle size.  NIR spectra were collected from 1350 nm to 2560 nm at 16 nm resolution.  NIR spectra were also collected on intact samples for the commercial cereals.  Three spectra were collected per sample.  Reference values for sucrose, glucose, fructose, and total sugars were obtained using HPLC.  Various preprocessing methods were applied to the spectral data before chemometric analysis.  Partial Least Squares (PLS) calibration models were created correlating the spectral data to sugar parameters. Results were shown below. 

Ground Cereal Samples

Sucrose	R² = 0.98	SECV = 1.93%
Glucose	R² = 0.94	SECV = 0.14%
Fructose	R² = 0.95	SECV = 0.25%
Total Sugars	R² = 0.98	SECV = 1.99%

Intact Cereal Samples 

Sucrose	R² = 0.97	SECV = 2.42%
Glucose	R² = 0.94	SECV = 0.20%
Fructose	R² = 0.92	SECV = 0.21%
Total Sugars	R² = 0.96	SECV = 2.48%

The results of this study were excellent and proved the feasibility of using NIR spectroscopy and calibration models to measure sugar parameters in breakfast cereals.  Results were similar for both the ground and intact cereal samples and especially when considering there were a smaller number of intact samples used, the study proved that in-line monitoring of sugars during the breakfast cereal manufacturing process is suitable. Interestingly, the comparison of both reference HPLC values and predicted values using the NIR spectroscopic calibration models with the sugar values on the labels in the commercial samples showed that seven of the sixty samples had a much higher sugar value than indicated on the label while one sample had a lower sugar value.  This difference reinforces the need for an online, fast, and non-invasive method like NIR spectroscopy for determining sugar parameters in breakfast cereals.  

In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer – DOAJ 

Classification of Cereal Bars Using Near Infrared Spectroscopy and Linear Discriminant Analysis 

There is increased demand among consumers for food with low calorie content.  However, distinguishing between the meaning of different labels like “natural”, “light”, “diet”, “organic” and “functional” can be quite confusing when trying to select a low calorie food.  Light and diet foods can be found in most supermarkets and are labelled as products with low fat, salt, protein, carbohydrates, or sugar contents.  Per the National Health Surveillance Agency (ANVISA) in Brazil, the term “light” can be used when the quantity of calories is at least 25% less than the conventional product.  “Diet” can be applied to foods with an absence of sucrose/glucose or foods indicated for diets with restrictions of certain nutrients, such as fat, carbohydrates, protein, and sodium.  Cereal bars were introduced around twenty years ago and have become very popular as a quick snack with low caloric and high nutritional value.  Cereal bars are often high in fibers, vitamins, and minerals and consumers often choose products based on the appearance, package description, and the nutritional information given.  However, incorrect information and/or misreading the label can often lead consumers to select cereal bars that are not suitable to their dietary needs.  Quality control in cereal bar manufacturing is done through physical and chemical tests which are often time-consuming, expensive, require the use of toxic chemicals and solvents as well as sample destruction, and are impractical for measuring a large number of samples.  NIR spectroscopy was examined for the purpose of classifying three different types of cereal bars: diet, conventional, and light.  A total of one hundred and twenty-one cereal bars were procured for the study.  Thirty-five were diet, forty-four were conventional, and forty-two were light.  All samples were crushed and sieved before NIR spectra collection.  Samples were scanned from 10000 cm-1 to 4000 cm-1 at a spectral resolution of 8 cm-1.  Sixteen scans were collected per reading and averaged into a single spectrum per sample.  Ninety samples were used as a calibration set and the remaining thirty-one samples were used for independent predictions.  Various pre-processing methods were applied to the spectral data before chemometric analysis.  Principle Component Analysis (PCA) was first performed to determine differences in the spectral data and for outlier determination.  Linear Discriminant Analysis (LDA) models were used to discriminate between the three types of cereal bars.  Numerous models were created using different pre-processing methods and wavenumber ranges selected using various algorithms and the spectra of the independent prediction set were used to classify those samples.  The best results are shown below. 

LDA Model Using Genetic Algorithm (GA) for Wavelength Selection 

28 out of 31 samples correctly classified 

Results from the different models greatly varied and indicated that wavenumber selection was very important in obtaining the best results.  The GA method is commonly used to generate solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection.  However, it must be noted that using different combinations of fifteen hundred wavelength areas extensively is creating a situation where the data fit must come into question.  More extensive calibration work would be needed to ensure that the classification was actually based on the cereal bar type and not just from fitting the wavelength range as the ideal set of independent variables.  The best model correctly classified twenty-eight of the thirty-one cereal bars, indicating that using NIR spectroscopy for classification of cereal bars has potential as an alternative method to traditional time-consuming and expensive methods for cereal bar quality control and analysis.  

Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis – ScienceDirect 

Introduction 

Corn is not only an important staple food for human consumption but is used to create many different types of products and by-products.  These include animal feed, biofuel, corn starch, corn oil, and many different forms of corn syrup.  More corn is produced than any other cereal plant worldwide. Sweet corn has become particularly appealing to consumers for its good taste and high nutritional content.   Corn is comprised of four parts: endosperm, germ, pericarp, and tip cap, all showing distinct characteristics and containing different portions of the precursors of corn products.  There are many different biotypes that can be grown in different parts of the world under varying climate conditions.  Demand for corn is increasing rapidly and market growth is expected to be strong over the next decade, especially for animal feed and corn starch products.  Corn is processed by either wet milling or dry milling. Wet milling separates the corn into separate components which can then be processed into various products.  Dry milling is similar to wheat flour production and is used mostly for producing flour and as the precursor for processing corn into ethanol.  Genetic engineering of corn has been going on for decades and always remains the subject of great debate.  Many transgenic varieties have been created with traits like herbicide, pest, and drought resistance.  Research continues to be conducted at a fast pace to improve the quality of corn and its products.  With demand continuing to grow and research moving forward at a rapid pace, there is a need for new testing methods to meet the challenges of optimizing corn breeding, harvesting, growing, and processing.  Traditional methods are often expensive, time-consuming, and impractical for use on a large scale.  One method which has shown potential for measuring parameters of interest in corn that is fast, non-invasive, and able to be implemented for large-scale testing is NIR spectroscopy. 

Analytes 

• Sweet corn cultivar sorting 
• Viable and non-viable supersweet corn seed sorting 
• Effects of varieties, producing areas, ears, and ear positions of single maize kernels on NIR spectra  
• Non-Structural Carbohydrates (NSC)  
• Water Soluble Carbohydrates (WSC) 
• In Vitro Organic Dry Matter Digestibility (IVOMD)  
• Organic Matter (OM) 
• Crude Protein (CP) 
• Neutral Detergent Fiber (NDF) 
• Acid Detergent Fiber (ADF) 
• Starch 
• Nutritional value of silage from Brazil  
• Corn seed germination rate 
• Detection of fusarium infused diseased maize grains 
• Gross Calorific Value (GCV) 
• Cell wall residues 
• Degradability 
• Lignin content 
• Lignin structure  
• p-Hydroxycinnamic acids  
• Structural sugars 
• Transgenic vs. non-transgenic discriminant analysis 
• Macronutrient content as a tool for breeding selection 

Summary of Published Papers, Articles, and Reference Materials 

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis 

The sweetness of sweet corn is a major factor in consumer satisfaction and breeders are always working to breed sweeter cultivars.  It is usually consumed when immature because of high nutrition and increased sweet flavor.  A uniform maturity time is important to choose the optimal harvest time and then to obtain a good shelf-life time as the sweet flavor changes quickly after harvesting.  Different cultivars of sweet corn vary in the maturity cycle, even when planted under the same conditions.  Mixing of cultivars is undesirable for a number of reasons, such as differences in maturity cycle times, variation in nutritional value, and resistance to diseases and pests.  The purity of a seed cultivar is defined as the ratio of seeds belonging to a cultivar to the total tested seeds.  Improving purity maximizes quality and yield, leading to increased economic benefits but traditional methods for determining cultivar purity like protein electrophoresis and DNA molecular markers are expensive, time-consuming, and impractical for large-scale use.  FT-NIR spectroscopy was examined for distinguishing between different cultivars of single-kernel sweet corn seeds.  Three hundred and eighty sweet corn seeds from each of two separate cultivars were procured for the study.  Initial examination was done by scanning a few seeds on both the embryo side and endosperm side of the seeds.  After visual examination of the NIR spectra, it was determined that the embryo side spectra would be used for the study.  All seeds were scanned from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals. Thirty-two scans were collected per reading and averaged into one spectrum per sample for a total of seven hundred and sixty spectra.  Various pre-processing methods were applied to the NIR spectra before chemometric analysis.  Principle Component Analysis (PCA) was performed for outlier detection and to determine differences in the spectral data.  After PCA, a genetic algorithm (GA) was applied to determine the feature wavelengths in spectral differences and one hundred and twenty-six wavelengths were selected.  Four separate classification algorithms were used with various wavelength ranges for modeling: K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machine Discriminant Analysis (SVM-DA).  The spectra were split into two groups: two-thirds for a calibration group and one-third for a validation group.   

SVM-DA 

Full Wavelength Range	99.59% Correct Classification
Featured Wavelengths	99.19% Correct Classification

The best results were obtained using the SVM-DA algorithm and as shown, there was over 99% accuracy using both the full wavelength range and featured wavelength range for the models.  This study proved the feasibility of classifying different corn cultivars using FT-NIR spectroscopy and the SVM-DA classification algorithm.  Using a classification model like this could be a method used in seed sorting machinery to select high-purity seeds of the same cultivar, helping to optimize product quality and yield in sweet corn. 

https://elibrary.asabe.org/abstract.asp?aid=50687 

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis 

Sweet corn has become a very popular vegetable in many countries because of pleasant flavor and high nutritional value.  However, low germination rate and seedling vigor of sweet corn seed have limited the development of the sweet corn industry.  High soluble sugar content and lower starch cause seeds to rapidly deteriorate compared to other corn seeds.  This likely occurs because less starch means less endosperm tissue can be reserved as an energy source for seed metabolism.  This effect is magnified with supersweet corn seeds and the high soluble sugar content also inhibits the drying of the seed crop in the field, often necessitating artificial drying after harvesting.  Proper temperature is essential during drying as it has a strong effect on germination and storability.  The sensitivity of supersweet corn seeds creates a need for determining seed quality to prevent non-viable seeds from entering the market and ultimately the planting and sowing process.  However, conventional methods like the germination test and tetrazolium test are time-consuming, expensive, destructive to samples, and impractical for implementing for large-scale testing.  NIR spectroscopy was examined as a method for determining viability in supersweet corn seeds.  Three hundred supersweet corn seeds from Huameitian No. 8, a well-known variety in China were procured for the study from South China Agricultural University.  More seeds were provided but three hundred seeds that were not cracked, broken, or discolored were chosen.  It is assumed that deterioration of supersweet corn seeds is caused by either excessive heating during the drying process or improper storage conditions.  In order to simulate this, one hundred seeds were subjected to deterioration by tempering the moisture to 20%, placement in a plastic bag and treating by incubation for seven days, and then dried back to the 20% moisture content. This process simulates artificial aging.  Another one hundred seeds were subjected to microwave treatment.  Both groups exhibited no changes to the naked eye and the remaining one hundred seeds had no treatment.  All seeds were scanned using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals on both the embryo and endosperm side.  Thirty-two scans were collected per reading and averaged into a single spectrum per sample.  After scanning, germination rate was checked for all seeds using the standard germination test method.  Seeds in the control group had a germination rate as high as 98% while the treated seeds had a 2% rate for the deterioration group and 5% for the microwaved group.  In reality, these seeds are considered non-viable because of their weak roots which would be unable to support healthy seedlings.  Various pre-processing methods were applied to the spectral data before chemometric analysis.  Six separate Partial Least Squares Discriminant Analysis (PLS-DA) models were created: Artificially Aged vs. Control, Heat-Damaged vs. Control, and All Damaged vs Control (for both embryo and endosperm spectra).   

Heat Damaged vs. Control (Embryo)	100% Viable	96.0% Non-Viable Classification
Heat Damaged vs. Control (Endosperm)	99.3% Viable	98.0% Non-Viable Classification
Artificially Aged vs. Control (Embryo)	100% Viable	98.0% Non-Viable Classification
Artificially Aged vs. Control (Endosperm)	99.3% Viable	94.0% Non-Viable Classification
All Damaged vs. Control (Embryo)	99.6% Viable	98.7% Non-Viable Classification
All Damaged vs. Control (Endosperm)	99.1% Viable	98.7% Non-Viable Classification

 

The prediction models determined that FT-NIR spectroscopy and PLS-DA classification models can be used to accurately detect non-viable supersweet corn seeds damaged by overheating and artificial aging.  Results were similar for both the embryo and endosperm sides of the seeds.  Results can be explained by the physical and chemical changes caused by the deterioration process.  There are other reasons that can cause seeds to be non-viable that have not been examined and published in an NIR spectroscopy study yet, such as frost damage during growth and natural aging.  Further work would entail examining some different types of seed damage as well as encompassing a larger sample set with different varieties and batches of seeds.  There is great potential to use NIR spectroscopy as a sorting tool for viable and non-viable corn seeds.  

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis – PubMed (nih.gov) 

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability 

Maize is an important source of food and industrial materials and demand for seeds is high, especially in China.  The quality of a maize seed is related to varieties and producing areas and identification of seeds is important to prevent adulteration.  Seeds from different provinces exhibit different characteristics even among the same variety, usually related to environmental factors like climate, daylight conditions, and soil.  While successful studies using NIR spectroscopy for identifying different varieties of maize seeds and wheat of the same species cultivated in different areas have shown distinct differences in NIR spectra that are sufficient for identification, no comprehensive study examining different factors and their degree of influence on NIR spectra of maize seeds has been conducted until now.  In this study, NIR spectroscopy was used to determine the degree of influence of genetic and environmental factors on large amounts of maize seeds of different varieties and from different producing areas.  A total of one hundred and thirty maize inbred lines harvested from Hainan and Beijing in China were procured for the study, all from the same harvest year.  Five ears were randomly selected from each inbred line harvested from Hainan.  Five seeds were collected from each ear from each position: ear tip, middle of the ear, and bottom of the ear.  For each line harvested in Beijing, twenty seeds were collected randomly.  In total, twelve thousand three hundred and fifty seeds were procured for the study.  Seeds were scanned with an FT-NIR spectrometer from 12000 cm-1 to 4000 cm-1 at 8 cm-1 resolution.  Twenty scans were collected per reading and averaged into a single spectrum per seed.  All seeds were scanned on the embryo side.  Various pre-processing methods were applied to the spectral data and the wavenumbers from 12000 cm-1 to 9000 cm-1 were eliminated from the data because of noise.  Principle Component Analysis (PCA) was first performed to examine differences in the NIR spectra.  The NIR spectral difference was calculated to determine the degree of influence of varieties, producing areas, ears, and different ear positions on the NIR spectra.  The one hundred thirty inbred lines from Hainan were used to calculate the influence from degree of variety.  After pretreatment, the difference between the spectra of each inbred line and the average spectrum was calculated and the average of these differences was the influence of degree of variety on the spectra.  Likewise, differences between the spectra from the two different producing areas was calculated to determine the degree of influence of producing areas.  Spectral differences between the spectra of different ears and different ear positions from the Hainan samples were also determined.  It was determined that wavelength bands from 1300 nm to 1470 nm, 1768 nm to 1949 nm, 2010 nm to 2064 nm, and 2235 nm to 2311 nm were strongly influenced by the producing area.  The degree of influence for the four factors was as follows: 45.40% for variety, 42.66% for producing areas, 8.22% for ears, and 3.72% for ear positions.  These results show that genetic differences among maize inbred lines are the main factor in differences in NIR spectra, with producing area accounting for a slightly smaller degree of influence.  The results provide a basis for variety authentication and breeding optimization.  Further study should be conducted on seeds from different harvest years to determine the degree of influence from year to year.   

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability (plos.org) 

NIRS Determination of Non-structural Carbohydrates, Water Soluble Carbohydrates and Other Nutritive Quality Traits in Whole Plant Maize with Wide Range Variability 

Forage maize is an important source of fodder for dairy farms in Spain, where the need for silage for cow feeding extends from five to seven months each year.  The nutritional content of forage is extremely important in determining the end product of cow milk as even small differences in nutritional content of forage can change the output and nutritional content of milk.  These parameters include non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). NSC has a strong influence on the utilization of other nutrients while WSC is the substrate for growth of lactobacilli needed for acid lactic fermentation.  The other parameters are all important for energy and digestibility in cow forage.  Current methods for testing these parameters are time-consuming, expensive, and impractical for implementation on a large scale.  NIR spectroscopy was examined for determining components of forage maize.  Maize whole plant samples were collected over an eight year period from different locations in Spain and in diverse environmental conditions.  To further expand the diversity of the sample set, samples were also collected from different genetic diversity sources, different countries, different tillage systems, and different maturity stages.  Over three thousand samples were scanned using an NIR spectrometer from 1100 nm to 2500 nm.  Various preprocessing methods were applied to the NIR spectra before chemometric analysis.  All samples were analyzed for the parameters of interest using traditional methods.  In total, four hundred and fifty samples were chosen for a calibration set and eighty-seven were chosen for a validation set.  Sample selection was based on expanding the variability of the spectra by adding appropriate outlier samples to previous calibrated equations after determination of reference values.  Partial Least Squares (PLS) calibration models were created to correlate the NIR spectra to the parameters of interest.  Results are shown below. 

OM	R² = 0.91	SEC= 0.23 g/100 g Dry Matter
CP	R² = 0.93	SEC= 0.29 g/100 g Dry Matter
NDF	R² = 0.91	SEC= 1.61 g/100 g Dry Matter
ADF	R² = 0.92	SEC= 0.98 g/100 g Dry Matter
IVOMD	R² = 0.77	SEC= 2.30 g/100 g Organic Matter
WSC	R² = 0.90	SEC= 1.34 g/100 g Dry Matter
NSC	R² = 0.87	SEC= 2.57 g/100 g Dry Matter
Starch	R² = 0.90	SEC= 2.67 g/100 g Dry Matter

The independent validation set was used for predictions to validate the models and out of the parameters, WSC and NSC showed the best results with good accuracy obtained in the predicted results.  Starch, NDF, ADF, OM, and CP showed accurate results as well.  The prediction results for IVOMD were not as good as the rest of the parameters and it was speculated that error in the reference method may have contributed to the higher prediction error.  Still, the results were considered sufficient for screening purposes and to distinguish between high and low values for IVOMD.  This study proved the feasibility of using NIR spectroscopy to determine nutritive components in forage maize and the potential to replace traditional time-consuming and expensive reference methods for determining these parameters.  

463-471_3316-391-12_NIRS (ciam.gal) 

Nutrition Value of Silage from Corn Hybrids in the State of Mato Grosso, Brazil 

Rainfall in the Brazilian savanna between October and March causes considerable seasonality in forage production and thus difficulties in maintaining product regularity and income of producers.  Evaluating the chemical composition of silage of corn cultivars is very important because it determines the food quality available for animal intake, especially in the case of neutral detergent fiber (NDF) because reduced NDF increases dry matter (DM) digestibility.  In this practical application, NIR spectroscopy was used to determine the nutritional value of corn silage from different hybrids cultivated on an experimental farm in Brazil.  Twenty-three different hybrid corn varieties from different seed companies of three repetitions each were planted for the study.  Harvesting and slicing of corn plants for ensiling occurred one hundred days after plant emergence when the grains were at the hard flour stage.  Forage was cut at an average particle size between two and three cm and placed in sealed silos for ninety days.  After opening the silos, samples were collected from the middle of each silo.  A portion of each sample was set aside for pH and ammoniacal nitrogen tests.  Samples were scanned using an NIR spectrometer and calibration models were used to determine dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and the minerals Ca, P, K, and Mg rates from the NIR spectra.   From these parameters, rates of total digestible nutrients (TDN), net lactation energy (NLE), net energy gained (NEg), net energy for maintenance (NEm), digestibility in vitro of DM (DIVDM) and dry matter intake (DMI) were calculated.  All pH and ammoniacal nitrogen tests showed expected values.  Twelve of the hybrids showed lower NDF and thus higher estimated DMI values.  This practical application showed the benefits of using NIR spectroscopy as a tool to determine variation in corn hybrid silage as performing traditional reference tests to determine the parameters of interest in this study would have been expensive, time-consuming, and required the use of expensive chemicals and solvents.   

https://www.scielo.br/j/asas/a/5gWjXTymZX6TjdhshwFW5wQ/?format=pdf 

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination 

Seed viability and germination rate are crucial parameters in planning agricultural production.  It can be significantly influenced by both ecological factors, biochemical metabolism of the seed, and improper storage after harvesting.  Traditional methods for determining these parameters include tetrazolium, conductivity, immunoassay, and germination tests which are not only expensive, time-consuming, and require the use of toxic chemicals and solvents but can also be very dependent on the experience and sensitivity of the technicians performing the tests.  There is a need for a fast, non-invasive, and cost-effective testing method to determine corn seed germination rate and NIR spectroscopy was examined for this purpose.  Eighteen different commercial samples of corn seed were procured for the study.  Fourteen of the eighteen samples belonged to the same genotype of various ages that were stored for periods ranging from three months to two years under uncontrolled room conditions.  Samples were scanned using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 using 4 cm-1 resolution.  Thirty-two scans were collected per reading and averaged into one spectrum.  This process was done three times per sample for a total of fifty-four spectra.  Germination rate of the seeds was determined using a standard International Seed Testing Association (ISTA) method.  Seeds were placed in moist germination paper, rolled, placed in a plastic container, and incubated at 25°C for seven days.  When the emerging radicle was at least 2 mm, the seed was considered germinated.  A significant variation was detected between corn genotypes in terms of germination rates and made the effect of seed storage period clear on germination rate.  Rates varied from 0% for a non-viable seed to 18% for the slowest germinating seed to 100% germination.  A Partial Least Squares (PLS) calibration model was created correlating the germination rate to the NIR spectra. Results are shown below. 

Germination Rate

R² = 0.98%

SEP= 4.61%

The results of this study show promise for determining the germination rate of corn genotypes using NIR spectroscopy and a calibration model.  However, it must be noted that the sample set is very limited and in order to use this model in a practical setting, more samples and multiple genotypes must be added to the calibration set.  With such a small sample set, it is unclear exactly what parameters the calibration model is using to determine the basis for the correlation.  It is recommended that a sample set with a minimum size of one hundred samples using multiple genotypes be used before using this application in a practical setting.  

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination | TR Dizin 

An Approach for Identification of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers 

Fusarium is a genus of fungi that is widely distributed in soil and associated with plants.  While most species are harmless, some produce mycotoxins in cereal crops that can be toxic to humans and animals if consumed.  Fusarium spp. Fusarium verticillioides is one such disease and it is important to determine that maize grains are free of this disease before entering the food chain.  Traditional testing methods typically involve chromatography and while accurate, they are expensive, time-consuming, and impractical for large-scale testing use.  Fluorimetric testing can be used as well but this method still takes about fifteen minutes to examine one single seed.  A need exists for a fast, non-invasive testing method that can test large amounts of maize grains for disease and NIR spectroscopy was examined for this purpose.  A total of nine hundred grains from the most popular variety of corn in Bulgaria were procured for the study.  Samples were sorted into healthy and diseased grains and were scanned on both the endosperm and embryo sides.  NIR spectra were collected using an NIR spectrometer from 400 nm to 2498 nm at 2 nm intervals.  Each sample was scanned three times for a total of twenty-seven hundred spectra.  Sample spectra were divided into a calibration set and validation set with six hundred samples chosen for calibration and three hundred for validation.  Various preprocessing methods were applied to the spectral data before chemometric analysis. Three different classification algorithms were used to classify the grains based on NIR spectra: Soft Independent Modeling of Class Analogy (SIMCA), K-Means, and Probabilistic Neural Network (PNN).   

Endosperm 

SIMCA	Healthy – 98.7%	Diseased – 96.0%
K-Means	Healthy – 95.3%	Diseased – 90.6%
PNN	Healthy – 99.3%	Diseased – 98.7%

Embryo 

SIMCA	Healthy – 99.3%	Diseased – 93.3%
K-Means	Healthy – 94.6%	Diseased – 92.0%
PNN	Healthy – 99.3%	Diseased – 99.3%

Best results were obtained using the PNN algorithm with the NIR spectra of the embryo side of the seeds. Spectra of the validation set were used with the classification algorithm to validate the model.  However, it must be noted that the threshold of detection of mycotoxin concentration is far too low to directly detect it using NIR spectroscopy.  It is quite possible that the mycotoxin concentration is affecting chemical and physical parameters in the seeds that can be detected using NIR spectroscopy and the results of this study reflect the changes in these parameters that occur in diseased maize grains.  In order to properly validate a study of this nature, a thorough examination of the wavelengths showing differences in the NIR spectra of the two groups of seeds and tests on the nutritional content of the two groups of seeds is recommended. 

www.clbme.bas.bg/bioautomation/2010/vol_14.2/files/14.2_03.pdf 

Gross Calorific Value Estimation for Milled Maize Cob Biomass Using Near-Infrared Spectroscopy 

Maize cob is the waste product after the maize seed is removed.  While it has been used as a fertilizer, it takes a long time to decompose and is not popular among farmers for this reason.  However, maize cob can be used as a biofuel.  When burned, it has a calorific value of approximately 17,000 kJ/kg and has a long burning time as well.  An important measurement in waste residues for biofuel use is gross calorific value (GCV), the total energy released in the burning process.  This particular measurement takes the latent heat of vaporization of water into account during the combustion process.  The traditional method for determining GCV is a bomb calorimeter which is expensive and takes about thirty minutes per sample, making it impractical for large-scale testing.  A need exists for a fast, non-invasive, and cost-effective method to determine GCV in maize cob and NIR spectroscopy was examined for this purpose.  Sixty samples of maize cobs were collected from different growing areas for the study.  After harvesting, samples were crushed and dried to a constant weight in an oven.  Samples were scanned using an FT-NIR spectrometer from 12500 cm-1 to 3600 cm-1 at 8 cm-1 resolution.  Each sample was scanned thirty-two times per reading and the scans were averaged into a single spectrum per sample.  Out of the sixty sample spectra, fifty were chosen for the calibration set and the remaining ten for a validation set.  After scanning, reference tests were performed on 0.5 g of each sample using a bomb calorimeter to determine GCV values.  Various pre-processing algorithms were applied to the NIR spectra before chemometric modeling.  A Partial Least Squares (PLS) calibration model was created correlating the NIR spectra to GCV. Results are shown below. 

GCV

R² = 0.83

RMSECV= 91 J/g

Modeling results showed good correlation and the independent validation set was used to confirm the validity of the model.  The RMSECV is quite low considering that each sample has a GCV in between 17000 J/g and 18000 J/g.  It is possible that using only 0.5 g for the reference tests contributed to a lower correlation coefficient as the variation between that sample size used for the reference test and the sample scanned with the spectrometer may have been significant. The sample set was limited and before using this model in a practical setting, more samples from different growing areas and different varieties of maize cob should be added to the calibration set to make the model more robust and confirm the validity of the calibration.  Overall, the results show promise for using NIR spectroscopy as a fast, non-invasive, and cost-effective method for determining GCV in maize cob with the potential to replace traditional time-consuming and expensive reference methods.  

(PDF) Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy (researchgate.net) 

Water Deficit Responsive QTLs for Cell Wall Degradability and Composition in Maize at the Silage Stage 

In order to use lignocellulosic biomass for animal feed or biorefining, optimization of the degradability of the material is required.  Much work is put into adapting biomass crops to changing climate and in particular to drought resistance.  Lignocellulosic biomass consists primarily of cell wall polymers.  Few studies have been conducted that use quantitative trait loci (QTL) to determine agronomical and cell-wall related traits related to water deficit.  In this study, the mapping power of a maize recombinant inbred line population was combined with NIR spectroscopy calibration models to track the response to water deficit of traits associated with biomass quality.  Over three separate years, the inbred line population was cultivated under two contrasted water regimes and harvested at silage stage.  NIR predictive equations were established for various biochemical cell wall related traits, such as cell wall residues, degradability, lignin content, lignin structure, p-Hydroxycinnamic acids, and structural sugars.  Results showed that cell wall degradability and β-O-4-linked H lignin subunits were increased in response to water deficit, while lignin and p-coumaric acid contents were reduced. A mixed linear model was fitted to map QTLs for agronomical and cell wall-related traits. These QTLs were categorized as “constitutive” (QTL with an effect whatever the irrigation condition) or “responsive” (QTL involved in the response to water deficit) QTLs. Fifteen clusters of QTLs encompassed more than two-thirds of the two hundred and thirteen constitutive QTLs and thirteen clusters encompassed more than 60% of the one hundred and forty-nine responsive QTLs.  The results showed that water deficit favors cell wall degradability and that breeding of varieties that show improved resistance to drought and biomass degradability is possible.  NIR spectroscopy proved to be a powerful tool in this study by enabling the quick analysis of the various traits needed to determine the effect of water deficit on the maize samples. 

Water Deficit-Responsive QTLs for Cell Wall Degradability and Composition in Maize at Silage Stage – PubMed (nih.gov) 

Screening of Transgenic Maize Using Near Infrared Spectroscopy and Chemometric Techniques 

Plant breeding uses molecular biology to produce new crop varieties and lines by using genetic engineering to introduce desirable traits into plants.  One important technique in breeding is selection, the process of effectively propagating plants with desirable traits and eliminating those with less desirable traits.  Breeders must screen large populations of crops to find plants with desired traits.  Traditional screening methods used for this purpose are DNA and protein based, such as polymerase chain reaction (PCR) and microarrays.  Such methods are time-consuming, expensive, and impractical for use when studying large numbers of samples, especially in the procedure of leaf DNA extraction.  NIR spectroscopy was examined for the purpose of classifying transgenic and non-transgenic maize plants.  Seeds of transgenic maize created with both herbicide and insect tolerant traits along with seeds from its parental line were procured for the study.  Seeds were sown and grown in a greenhouse for two months.  The second or third leaf that formed from each plant was selected for NIR sampling.  Before NIR scanning, PCR was used to check the integrity of the copies of the genes introduced during the breeding phase.  In total, one hundred and sixty-three of each of the transgenic and non-transgenic leaves were chosen for NIR scanning.  Samples were scanned using an NIR spectrometer from 900 nm to 1700 nm. Each leaf was scanned three times and the three scans were averaged into one spectrum.  Various pre-processing methods were applied to the spectral data before chemometric analysis.  Principle Component Analysis (PCA) was performed on the NIR spectra to analyze spectral differences and wavelength ranges that were relevant to differences in the spectra.  A total of five separate classification algorithms were applied to build discrimination models that can separate the transgenic and non-transgenic samples using the NIR spectra.  Models were created using both the full wavelength range and sensitive wavelengths identified from PCA.  The best results are shown below: 

Extreme Learning Machine (ELM)

95.20%

In order to validate the models, cross-validation was performed by removing sample spectra from the models and then classifying the samples based on the spectra that were removed.  The best results were obtained with the ELM algorithm using the full wavelength range.  The sensitive wavelength range models showed worse results.  It is common for natural products to show variations in NIR spectra that are not related to the parameters of interest and models using the full wavelength range for agricultural products in particular need a large wavelength range to be robust and predict accurately.  The potential was demonstrated for using NIR spectroscopy as a screening tool for transgenic plants that could replace expensive, time-consuming, and slow traditional reference methods.  

https://www.semanticscholar.org/paper/Screening-of-transgenic-maize-using-near-infrared-Feng-Yin/ddb820adedd480685f6677fb82ab65891ac92855 

Grain Quality of Drought Tolerant Accessions Within the MRI Zemun Polje Maize Germplasm Collection 

Maize is among the three most widely grown crops in the world. Breeders are conducting research to identify superior genotypes, particularly in relation to drought tolerance as drought is one of the most important factors that limits production of maize.  Even in areas where the average rainfall is sufficient for maize growing, the distribution of rainfall can be insufficient and causes yield loss in the crops.  The incorporation of genetic research in breeding programs that can create lines that are more drought resistance and thus having most stable yields is growing rapidly, but current testing methods for determining genetic lines with these traits are impractical for large-scale use.  In this study, NIR spectroscopy was used as a tool for determining nutritional content of forty different accessions that were created from an elite drought tolerant core gene bank from multiple inbred lines, introduced populations, and landraces.  The purpose was to determine if macronutrient content gain among the different generic groups could be correlated to genetic gain and thus identify these groups as potentially favorable sources for a specific trait, in this case drought tolerance.  The forty different accessions from the core were grown, multiplied, and at least eighty ears of maize were collected per multiplied population.  Samples were scanned using an NIR spectrometer and calibration models were used to determine values for oil, protein, and starch.  It was noted that the oil, protein, and starch contents were significantly higher in the introduced populations than for the landraces.  Oil in particular showed the greatest progress from the selection based on the expected genetic gain at 14.74%, indicating that the greatest progress in breeding could be determined from increased oil content with accessions from an unknown group.  The potential was shown in this study to use NIR spectroscopy as a tool for determining macronutrient content in maize which can then be used to identify accessions with favorable traits to assist breeders in selecting plants with desirable qualities for improved breeding.   

Grain quality of drought tolerant accessions within the MRI Zemun Polje maize germplasm collection – Dialnet (unirioja.es)