Introduction
Rice is the most widely consumed food in the world and approximately one-half of the world population is wholly dependent on rice, especially in Asia. It is estimated that one-fifth of all calories consumed worldwide come from rice and proper planting, harvesting, processing, storage, and transport are essential. Starch and protein are two important nutritional components in rice. Starch content is measured by amylose, the linear and helical molecule that comprises 20% to 25% of starch in rice. Both breakdown and setback viscosities have been correlated both positively and negatively with sensory attributes of rice after it has been cooked, such as stickiness, firmness, and hardness, making these important measurements in final product quality. Moisture content of rice should be between 20% and 25% at the time of harvesting and drying is important before storage to reduce fungal growth and insect infestation. Other important nutritional parameters include antioxidant activity and Gamma Oryzanol, which measure organic molecules that promote health by protecting cells from damage caused by free radicals and reactive oxygen species that may exert harmful metabolic effects. With the vast amount of rice produced, authentication of different species and brands of rice is extremely important. The same species of rice can vary greatly in nutritional value from brand to brand, making higher-quality brands subject to adulteration with cheaper ones. The development of hybrid rice that produces more plant yield also presents challenges for monitoring and identifying species. Discrimination of transgenic rice as well as monitoring wall polymer features and biomass saccharification are important as research and production of transgenic rice continues to increase. Insect infestation is a big problem for rice producers and they often use far more pesticide than needed for fumigation because there is no easy way to determine the exact level of pest infestation. Using too much pesticide wastes product and can also create health issues for the rice consumer. There is a need to develop fast, non-invasive testing methods to meet the evolving challenges in producing quality rice. One such method that has been examined is NIR Spectroscopy.
Analytes
- Amylose
- Protein
- Breakdown viscosity
- Setback viscosity
- Moisture
- Total Phenol Content (TPC)
- Radical scavenging activity by DPPH
- Species and brand authenticity
- Hybrid rice ID
- Chlorophyll content of leaves
- Transgenic rice discrimination
- Wall polymer features and biomass
- saccharification in transgenic rice
- Weevil quantity in rice stock
Summary of Published Papers, Articles, and Reference Materials
Prediction of Some Quality Properties of Rice and its Flour by NIR Analysis
Bulk samples of the Iranian rice variety Kharaz were procured for the study. Initial moisture content was determined and then paddies were dried using a laboratory dryer. A portion of each sample was crushed with a husk and milled. Five grams of each sample were scanned in a rotating cup from 870 nm to 2450 nm in reflectance mode at 6.5 nm increments. Each sample was scanned three times with sample repacking for each individual scan and the three scans per sample were averaged into a single spectrum. This process was conducted for both milled rice and rice flour from the samples. In total, one hundred eleven samples (eighty-four for calibration and twenty-seven for validation) for rice flour and one hundred and nine (eighty-one for calibration and twenty-eight for validation) were used for the study. Reference tests were conducted on each sample to determine amylose content, protein content, breakdown visosity, and setback viscosity. Principle Component Analysis (PCA) was performed for outlier determination and various pre-processing methods were applied to the spectral data before chemometric analysis. The spectral data and reference values were used to create Partial Least Squares (PLS) models correlating the spectra to the parameters of interest.
Grain
| Amylose | R² = 0.881 | RMSEC = 0.303% |
| Protein | R² = 0.948 | RMSEC = 0.27% |
| Breakdown Viscosity | R² = 0.984 | RMSEC = 2.59 RVU |
| Setback Viscosity | R² = 0.927 | RMSEC = 3.11 RVU |
Flour
| Amylose | R² = 0.851 | RMSEC = 0.393% |
| Protein | R² = 0.994 | RMSEC = 0.07% |
| Breakdown Viscosity | R² = 0.961 | RMSEC = 2.57 RVU |
| Setback Viscosity | R² = 0.962 | RMSEC = 1.33 RVU |
The models showed good correlation between the spectral data and parameters of interest. Independent predictions from the validation samples spectra proved the validity of the models. The results of this study demonstrated the potential to use NIR spectroscopy as a fast and non-invasive method for predicting amylose, protein, breakdown viscosity, and setback viscosity in both rice and flour.
Measurement of Moisture Content for Rough Rice by Visible and Near-Infrared Spectroscopy
Two separate spectrometers were procured to examine the feasibility of determining moisture content in rice samples. Three different types of rice were used: single kernel, multi-kernel, and cracked multi-kernel. Samples were collected and were dried at different levels ranging from 11.5% to 28.7% moisture. One spectrometer had a wavelength range from 400 nm to 1050 nm while the other had a range of 400 nm to 2498 nm. Both Multiple Linear Regression (MLR) and Partial Least Squares (PLS) algorithms were used to create models using the reference values for moisture and spectral data after various pre-processing methods. In total, seventy-two different models were created. The best results were shown using a PLS model with first derivative processing over the wavelength range from 400 nm to 2498 nm with an R² value of 0.97 and an SEC of 1.3% moisture. The results proved the feasibility of using NIR spectra and chemometric modeling to predict moisture content in rice.
Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics
Brown rice is known as a food with the potential to improve human health because it is high in antioxidative compounds which have the ability to both inhibit the formation and to reduce the concentrations of reactive cell damaging free radicals. Standard reference methods for measuring these compounds are time-consuming, expensive, and impractical for measuring a large number of samples. The potential for using NIR spectroscopy to measure antioxidant activity in rice expressed as Total Phenol Content (TPC) and Radical Scavenging Activity by DPPH – both expressed as Gallic Acid Equivalent (GAE), a measurement of the amount of phenolics in a substance – was examined. One hundred twenty-one brown rice samples were collected from five separate producing areas for the study. Samples were ground into powder before scanning. Diffuse reflectance spectra were collected for all samples from 12000 cm-1 to 4000 cm-1 with 4 cm-1 resolution and a scanning interval of 1.929 cm-1. Sixty-four scans were collected for each reading and averaged into one spectrum. Reference tests were performed on each sample to determine TPC and DPPH. Various pre-processing methods were applied to the spectral data before chemometric modeling.
| TPC | R² = 0.962 | RMSEP = 0.062 mg GAE/g |
| Radical Scavenging Activity by DPPH | R² = 0.974 | RMSEP = 0.141 mg GAE/g |
Good correlation was shown for both antioxidant parameters in the chemometric models. The wavelength ranges from 5600 cm-1 to 4800 cm-1 and 6400 cm-1 to 6000 cm-1 showed the best results for the TPC model and the ranges from 5200 cm-1 to 4400 cm-1 and 6400 cm-1 to 6000 cm-1 showed the best results for the DPPH model. While promising, it must be noted that the results here are for measuring very low concentrations of these parameters that are below the usual threshold of detection for measurements using NIR spectroscopy. It is possible that the models are making an indirect correlation of other parameters that are correlated with the antioxidant activity. While indirect correlations are acceptable if properly validated, more study will be necessary before using this application in a real-time, practical setting.
Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy
Gamma Oryzanol is a substance found in rice bran as well as wheat bran and in some fruits and vegetables. It is often extracted as rice bran oil and is considered valuable for its high nutritional value due to its mixture of antioxidant compounds. The feasibility of measuring Gamma Oryzanol in germinated brown rice using NIR spectroscopy was examined. Both rough rice samples and samples that were already germinated and purchased from local markets in Thailand were procured for the study. The rice was soaked in water at room temperature for either twenty-four or forty-eight hours and dried at intervals ranging from no drying time to thirty-six hours. Two hundred eighteen samples in total were used for the study. Samples were scanned in a rotating cup from 12500 cm-1 to 4000 cm-1 at 16 cm-1 resolution. Sixty-four scans were collected per reading and averaged into one spectrum. Various pre-processing methods were applied to the spectral data before chemometric modeling.
| Gamma Oryzanol | R² = 0.934 | RMSECV= 0.88 X 10-4 mg/100 g |
Different groups and pre-processing methods were used to create different Partial Least Squares models for different varieties and groupings of rough rice and rice purchased from markets. The results shown above were for the germinated rice purchased from markets. Other groups showed mixed results for correlation. While promising, it must be noted that the results here are for measuring very low concentrations of gamma oryzanol that are below the usual threshold of detection for measurements using NIR spectroscopy. It is possible that the models are making an indirect correlation of other parameters that are correlated with the Gamma Oryzanol concentration. While indirect correlations are acceptable if properly validated, more study will be necessary before using this application in a real-time, practical setting.
https://www.worldscientific.com/doi/pdf/10.1142/S1793545814500023
Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description
Black rice is a very important rice species in Southeast Asia and varieties often differ in nutritional value due to genetic and environmental factors. The quality and price of different brands can vary greatly and lower quality brands are often sold as higher quality brands. Current methods for determining the quality of black rice are either objective human sensory methods or by expensive methods that are impractical because of cost and the inability to implement them on a large scale, especially when considering that adulteration usually occurs in small town markets. The feasibility of discriminating between different brands of black rice using NIR spectroscopy was examined. A total of one hundred forty-two black rice samples from three separate brands was procured for the study. Samples were scanned in a rotating cup from 10000 cm-1 to 4000 cm-1 at 3.856 cm-1 resolution. Thirty-two scans were collected per reading and averaged into one spectrum. Principle Component Analysis (PCA) was used for exploratory analysis and three separate algorithms were used for classification analysis: Support Vector Data Description (SVDD), Nearest Neighbor Method (NNM), and Gaussian Method.
| SVDD | Specificity – 100% | Sensitivity – 94% |
The results using SVDD for a classification method to sort the three separate brands of rice using NIR spectra were excellent and proved the feasibility of the method. The other two algorithms showed good results as well. This study showed that NIR spectroscopy can be used to replace sensory and chemical analysis as methods for authenticating different brands of black rice. More samples and data analysis would be needed before implementing this method in a practical setting.
Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies
Vietnam is among the top five exporters of rice and it is estimated that there are more than one hundred thirty brands of rice on the market, with no single brand accounting for more than 3% of the total. The market is fragmented and sellers often mix a low quality brand of rice with a higher quality brand or even pass off a low quality brand as a high quality one. Current methods for determining rice authenticity are time-consuming, costly, can require extensive sample preparation, and are not suitable for large-scale measurements. Two separate varieties of rice from different regions were procured for the study – one known as higher quality (J85) and one known as lower quality (DT8). Adulterated samples were prepared by adding 5% and 10% by weight of DT8 to J85 rice. In total, seventy-two authentic and one hundred twenty-eight adulterated samples were used. Samples were scanned in a rotating cup from 740 nm to 1070 nm using a hand-held spectrometer at a 1 nm scanning interval. Three spectra were collected per sample. Various pre-preprocessing methods were performed on the spectral data. Partial Least Squares Discriminant Analysis (PLS-DA) was performed on different classifications of the data, including pure vs. both groups of adulterated samples as well as pure vs. 5% adulterated and pure vs. 10% adulterated.
| PLS-DA | 84% correct classification of pure samples vs. 10% adulterated |
The results here show that NIR spectroscopy is a viable method for classifying pure high quality rice samples and adulterated samples at 10% adulteration. The classification results were worse when using just the 5% adulterated samples and both the 5% and 10% adulterated samples, indicating that the method is best suited for detecting 10% and higher levels of adulteration. It is almost certain that better results would be achieved using a spectrometer with a longer wavelength range and including samples at a level of adulteration higher than 10%. The short wavelength leaves out areas of the NIR spectrum that likely show spectral differences that could be used for classification and using samples that are adulterated at a level higher than 10% would increase the range for the discriminant analysis and provide a basis for better discrimination between sample groups.
Vis/NIR Reflectance Spectroscopy for Hybrid Rice Identification and Chlorophyll Content Evaluation for Different Nitrogen Fertilizer Levels
The use of hybrid rice has become prominent to increase yield in fields where rice is grown. New higher-yield varieties are being continuously developed. Nitrogen is an important nutrient indicator for crops and is closely correlated with the chlorophyll content of leaves as well as the photosynthetic ability of crops. NIR spectroscopy was examined as a method for classifying varieties of hybrid rice and six different nitrogen fertilizer levels as well as quantifying chlorophyll content in rice leaves. The five varieties of hybrid rice were all cultivated in one experimental field in China which was divided into thirty separate zones. During the entire growing period, six separate levels of nitrogen fertilizer were provided in the different zones. When the rice reached the maturity stage, five whole plants were collected from each zone. The plants were placed into pots filled with water to prevent the leaves from drying. Four rice leaves at different heights were retrieved from each plant, making for a total of six hundred leaves. Leaves were scanned using a portable spectrometer from 250 nm to 2200 nm. Various preprocessing methods were applied to the spectral data before chemometric analysis. The Support Vector Machine (SVM) algorithm was applied to identify the five varieties of hybrid rice and the six levels of nitrogen fertilizer. Some success was achieved in classifying the five hybrid rice varieties, but three of the varieties had the same female parent plant and these three varieties were often misclassified amongst each other. There was also error in classifying the hybrid varieties at the highest nitrogen level, which likely occurred because the excessive nitrogen level resulted in abnormal growth. Better results were achieved for classifying based on nitrogen level, especially when classifying the no nitrogen plants vs. plants that do have nitrogen. This classification resulted in 100% success using an independent validation set. The spectral data and chlorophyll content values were used to create a Partial Least Squares (PLS) model correlating the chlorophyll content to the NIR spectra.
| Chlorophyll Content | R² = 0.978 | RMSECV = 0.506 SPAD |
The reference values for chlorophyll content were determined using an SPAD meter, which measures leaf transmittance at a few individual wavelengths to determine chlorophyll. Good correlation was shown from the chemometric model and an independent validation set confirmed the prediction results. This study proved the feasibility of using NIR spectroscopy to identify rice varieties and evaluate nitrogen fertilizer levels. More work will be necessary by adding more sample varieties before implementing this method in a practical setting.
Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis
Genetically modified foods are continuously researched for potential improvements such as resistance to disease, pests, and herbicides as well as increased nutritional content. The use of such foods is both highly regulated and controversial as there are concerns about human health and safety, environmental impact, and economic issues. Traditional detection methods for transgenic rice are expensive, difficult to use, and impractical for large-scale use. NIR spectroscopy was examined as a method for classifying wild rice and transgenic rice as well as classifying the same variety of transgenic rice transformed with the protein gene and regulation gene. The wild type rice Zhonghua 11 (ZH11) was used as the transgenic material. Two single copy transgenic rice lines were developed using the OsTCTP and Osmi166 genes into ZH11 and designated as TCTP and mi166. All rice lines were planted, harvested, and dried in the same field under the same conditions. A total of one hundred ninety-two rice grains were used for the study. Spectra were collected from 10000 cm-1 to 4000 cm-1 at 8 cm-1 resolution. Thirty-two scans were collected per reading and averaged into one spectrum. Three grains were used and repositioned three times for each sample for a total of three hundred seventy-six spectra. Principle Component Analysis (PCA) was applied for initial discrimination analysis and outlier detection. Partial Least Squares Discriminant Analysis (PLS-DA) was applied to discriminate between the wild vs. transgenic rice and the two separate transgenic lines.
PLS-DA
| Wild rice vs. Transgenic Rice | Correct Classification: 100% |
| TCTP vs. mi166 | Correct Classification 100% |
The results of this study were excellent and proved the feasibility of discriminating wild rice from transgenic rice as well as the same variety of transgenic transformed with different genes. A validation set was used to prove the validity of the calibration models. The potential was demonstrated for using NIR spectroscopy as a discrimination method for transgenic rice.
A precise and consistent assay for major wall polymer features that distinctively determine biomass saccharifcation in transgenic rice by near-infrared spectroscopy
The genetic modification of plant cell walls is considered to reduce lignocellulose recalcitrance in bioenergy crops. It is important to develop a precise and rapid assay for the major polymer features that affect biomass saccharification in transgenic plants. In this study, the feasibility of using NIR spectroscopy for predicting biomass enzymatic saccarification and major polymer wall features was examined. Two hundred forty-six transgenic rice plants and one wild rice plant were procured for the study. Before NIR spectra of the rice straws were collected, wall polymer features and biomass enzymatic saccarification were determined after alkali pretreatment. Correlation analysis indicated that crystalline cellulose and lignin levels negatively affected the hexose and total sugar yields released from pretreatment and enzymatic hydrolysis in the transgenic rice plants. The arabinose levels and arabinose substitution degree (reverse xylose/arabinose ratio) exhibited positive impacts on the hexose and total sugars yields. After this correlation analysis was performed, all samples were scanned from 400 nm to 2500 nm. Calibration models were created for different wall polymers and biomass saccarification parameters.
Wall Polymers
| Cellulose | R² = 0.88 | SECV = 1.59% Dry Matter |
| Hemicellulose | R² = 0.84 | SECV = 1.20% Dry Matter |
| Lignin | R² = 0.80 | SECV = 0.75% Dry Matter |
Biomass Saccarification
| Pretreatment | R² = 0.98 | SECV = 0.42% Total |
| Enzymatic Hydrolysis | R² = 0.98 | SECV = 1.38% Total |
| Total Sugar Released | R² = 0.91 | SECV = 1.38% Dry Matter |
| Fermentable Hexoses | R² = 0.97 | SECV = 2.20% Total Hexoses |
All models showed good correlation for the wall polymer features and biomass saccharification parameters. A rapid and precise screening method for biomass samples could be groundbreaking in determining a strategy for genetic modification of plant cell walls. Cellulose and hemicellulose modification and cell wall remodeling in transgenic rice lines could greatly improve biomass enzymatic digestibility in rice and the NIR method studied here provides a potential method for rapid screening of the parameters of interest.
Feasibility study on estimation of rice weevil quantity in rice stock using near-infrared spectroscopy technique
Premium grade rice requires that nearly all grains be perfectly whole with a minimum amount of foreign particles. Rice weevils are especially detrimental and can degrade premium rice into low-quality rice. The current practice is for rice millers to use excess fumigation to eliminate weevils, a practice that not only wastes product but can also leave excess pesticide residues that can cause health problems when the rice is consumed. There is a need for a rapid, low-cost method to determine the level of weevil infestation in rice and NIR spectroscopy was examined for this purpose. A total of sixteen hundred and eighty samples of both milled Hommali rice and brown rice Hommali rice were procured for this study. A total of twenty different levels of weevil infestation ranging from ten to two hundred rice weevils per sample in increments of ten were prepared. For each sample, rice was added to the weevils to make a 100 g portion and then each sample was gently mixed twenty times. A sample with no weevil infestation was included as well. Samples were scanned from 780 nm to 2500 nm at 0.5 nm scanning interval. Sixty-four scans were collected and averaged into a single spectrum. Further averaging was done by scanning ten portions of each sample and averaging those ten spectra into one spectrum. Various pre-processing methods were applied to the spectral data. Two separate Partial Least Squares (PLS) calibration models were created correlating the spectral data to weevil infestation level: one for milled rice and one for brown rice.
| Milled Rice Weevil Infestation Level | R² = 0.96 | RMSEP = 10.4 |
| Brown Rice Weevil Infestation Level | R² = 0.90 | RMSEP = 18.7 |
The results of this study demonstrated the potential to use NIR spectroscopy as a screening tool for estimating the level of weevil infestation in both milled and brown rice. Having a rapid method for determining infestation level can enable millers to reduce the amount of pesticide used to eliminate weevils, resulting in both reduced product used and cost savings as well as lower pesticide residues in the rice, improving safety for rice consumers.