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.
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.
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.
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.
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.
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.
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.
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.
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.