Background: Wheat quality and safety are crucial for global food security and regional agricultural economies. Conventional analytical methods for assessing grain composition and quality-while accurate-are often time-consuming, destructive and resource-intensive. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive alternative that provides detailed chemical and physical insights. Recent advancements in machine learning have enhanced NIR&rsquo;s analytical capabilities by enabling the development of predictive models that accurately categorize, quantify and monitor grain quality parameters. These models-spanning from linear regression methods to intricate neural networks-enable researchers to discern significant patterns from spectral data, even amidst noise or overlapping signals. 
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Methods: This study used NIR spectroscopy and machine learning models to evaluate wheat quality across multiple Albanian regions during the 2023 harvest. NIR spectra were preprocessed using standard normalization and smoothing techniques. Machine learning models-including PLSR, SVM and random forest-were trained to predict key quality traits such as protein content and moisture. Model performance was validated using cross-validation metrics (RMSE, R&sup2;, accuracy), demonstrating the potential of NIR-ML integration for rapid, non-destructive grain assessment. 
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Result: Protein content ranged from 9.6% to 15.0%, gluten from 19.4% to 37.5% and starch from 66.3% to 71.5%, indicating consistent nutritional profiles across samples. Sedimentation index values (21.1-57.8 cm&sup3;) and moisture levels (9.8-12.5%) confirmed compliance with EU food safety standards. The integration of NIR spectroscopy with machine learning enabled rapid, non-destructive prediction of wheat quality traits, supporting scalable decision-making in agri-food systems.

Applications of Machine Learning Models in Near-infrared Spectroscopy for Small-grain Quality Control

<div style="text-align: justify;">Plant biomass is the basis for the calculation of net primary production. Estimation of fresh biomass in high throughput way is critical for plant phenotyping. Conventional phenotyping approaches for measuring the fresh biomass is time consuming, laborious and destructive in nature. Image analysis based plant phenotyping is very popular nowadays. Most of the approaches used projected shoot area from visual images (VIS) to estimate the fresh biomass. As water content has a significant effect on fresh biomass and water absorbs radiation at near infra-red (NIR) region (900nm to 1700nm), we have hypothesized that the combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR images were collected using LemaTec facility installed at Nanaji Deshmukh Plant Phenomics Center, ICAR-IARI, New Delhi-12. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. MATLAB software (version 2015b) was used for image analysis. The two image derived parameter viz. Green Leaf Proportion (GPR) from VIS image and mean gray value/intensity (MGV_NIR) from NIR image were used to develop the statistical model to &nbsp;estimate the fresh biomass in the form of Leaf Fresh Weight (LFW). The proposed approach significantly enhanced the fresh biomass estimation.</div>

<div style="text-align: justify;">Plant biomass estimation using image analysis and machine learning technique</div>

<div style="text-align: justify;">An ischaemia/reperfusion (I/R) injury in an organ can also cause damage to other organs. In this study, the effects of nizatidine were investigated on ovarian I/R injury and subsequent heart damage in rats. Rats were divided into groups; ovarian I/R (IRG), nizatidine 25 mg/kg+ ovarian I/R (NIR), and control (CG) groups. The NIR and IRG groups were given 2 hours of ischaemia followed by 2 hours of reperfusion by applying, and subsequently releasing, a vascular clip to the right ovarian artery. While oxidant levels in the IRG group were higher than NIR and CG groups, antioxidant levels were lower. Necrosis, congested blood vessels, haemorrhage, leukocyte infiltration and oedema were observed in the ovarian and heart tissues of the IRG group. These findings were reduced in the NIR group. Nizatidine suppressed oxidative stress in ovarian and heart tissues and may be useful in prevention of I/R&ndash;induced ovarian and cardiac injury.</div>

The effect of nizatidine on ovarian ischaemia/reperfusion injury in rats

Background: Rapid prediction with near infrared (NIR) spectroscopy on quality traits is pretty popular recently, for the convenience and simple operation. But to make good use of this technology, precise and suitable calibration equations are very important to get dependable result. In this study, we mostly refer to the building of the equation and how the pretreatment effect them.&nbsp;&nbsp; 
 
Methods: In this paper, near infrared (NIR) spectroscopy was used to simultaneously predict the quality traits of soybean, including oil content, protein content, oleic acid content, linoleic acid content, stearic acid content. Near infrared spectral data of a total of 112 samples is collected from given materials in Chongqing. Samples were scanned from 1000 nm to 2500 nm using a monochromator instrument (SuperNIR-2700). Calibration equations were developed from NIR data using partial least squares (PLS) regression with internal cross validation. In addition, in this study, we also cover the affection of different pre-treatments to the different calibration equations predicting different quality traits. And measure the effect with three indicators including R, SECV and RPD.&nbsp; 
 
Result: Eventually we find the most suitable combination of pre-treatments for each calibration equation predicting a certain trait soybean. The present study would lay the foundations of rapid detection of quality traits in soybean.

Establishment of Detection Model of Soybean Quality Traits by Near Infrared Spectroscopy

Soil moisture is an important parameter in agricultural and environmental studies and affects the exchange of water and energy at the boundary between the earth&rsquo;s surface and the atmosphere. Accurate estimation of spatial-temporal changes in soil moisture is critical for numerous studies in the agricultural sciences. Vegetation indices, based on combinations of digital data of reflected light in the visible and near-infrared range, are suitable for the detection of water stress in plants. Therefore, they are widely used to monitor and detect drought conditions, but their accuracy is dependent on different vegetation types. The main objective of this research was to develop a novel procedure for the estimation of soil moisture using multispectral remote measurements. This study has been done using a multispectral camera Survey 3W Red+Green+NIR, which records images in three spectral channels: green (550 nm), red (660 nm) and near-infrared (850 nm). Eleven soil samples with different water content were investigated. SNAP (Sentinel Application Platform) software was used to process the captured multispectral images. It allows an easy calculation of various vegetation indices. The suitability of the normalized difference vegetation index (NDVI) for the assessment of soil moisture was evaluated. The average NDVI values did not indicate a well-established trend in relation to the SWC. A new simple vegetation index NIR/Green was successfully used for the assessment of soil moisture. The new NIR/Green index gives consistent results in relation to the real soil water content and could be used for mapping of the soil moisture with multispectral cameras.

Estimation of Soil Moisture from Multispectral Remote Sensing Data

Background: This study aimed to develop and evaluate a rapid, non-destructive method for predicting phosphorus content in chickpea flour using near-infrared (NIR) spectroscopy combined with machine learning techniques. The research sought to identify the most effective wavelength range and modeling approach for accurate phosphorus content estimation. 
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Methods: NIR spectra was collected from 237 chickpea flour samples, of which 132 showed detectable phosphorus reflectance. The dataset underwent preprocessing techniques including noise elimination, data reduction and scatter correction. The interval Partial Least Squares (iPLS) algorithm was employed to identify the optimal wavelength range for phosphorus prediction. Five machine learning models-linear regression (LR), support vector regression (SVR), random forest (RF), decision tree regression (DTR) and neural network (NN)-were developed and compared using a dataset split into calibration (80%) and validation (20%) sets. Principal component analysis (PCA) was used for dimensionality reduction. 
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Result: The most effective wavelength range for phosphorus prediction was identified as 1520-1658 nm. The Linear Regression model demonstrated the best performance on the testing data, with an R&sup2; of 0.945, Root Mean Square Error (RMSE) of 0.044 and residual standard error (RSE) of 0.045. All models except RF and DTR showed excellent predictive capability (RPD&gt;4.1). The Decision Tree Regression model performed best on the validation set, with an R&sup2; of 0.875 and RMSE of 0.079. The study confirmed the potential of NIR spectroscopy combined with machine learning as a rapid and accurate method for phosphorus content prediction in chickpea flour.

Analysis of Phosphorus Content in Chickpea Samples using Near-infrared Spectroscopy and Machine Learning Models

<div>Background: Among the pulse crop world chick pea known as important pulse crop which is grown and utilized in the world. This crop nutritionally rich in the carbohydrates, protein which has superior quality protein than the other pulse crop. This pea consists of lots of essential amino acids except the sulphur containing amino acids. So that it is a good combination of diet with the cereals. In the plants mostly carbohydrates stored in the starch form but also followed by oligosaccharides and reducing and non-reducing sugars. In this crop lots of nutritionally rich substances but it has low amount of fats and large amount of unsaturated fatty acid which is very beneficial to the heart patient. The minerals which are present in chick pea are Calcium, magnesium, phosphorus and especially potassium. The vitamins which are present in this pulse such as riboflavin, niacin, thiamin, folate and the vitamin A precursor, &acirc;-carotene. The pulses also have anti-nutritional factors which can be minimized by the using of lots of cooking methods. 
 
Methods: Through NIRS-2500 evaluated twenty samples of chick pea. It is an instrument through which Near Infrared (NIR) analysis a spectroscopic technique that makes use of the naturally occurring electromagnetic spectrum. This works in the region of the spectrum defined by wavelengths between 700nm and 2500nm. All the cultivars were found to cluster in major four groups on the basis of principal component analysis. The result showed the diversity between nutritional and antinutritional factors in the cultivars that could be further used by plant breeders to develop superior genotypes. The chick pea has lots of advantages which cure the several diseases such as cardiovascular disease, type 2 diabetes, digestive diseases and some cancers. &nbsp; 
 
Result: Here we study twenty samples of chick pea in which the biochemical composition of this crop consists of protein was varied from 22.12% to 24.42%, sulphur containg amino acids ranged from 0.15 to 1.25% and Tryptophan was ranged from 0.63 to 1.38% which was analyzed by NIRS-2500.</div>

<div>Evaluation of the Nutritional Quality and Health Benefits of Chickpea (Cicer arietinum L.) by using New Technology in Agriculture (Near Infra-red spectroscopy-2500)</div>

<div>Background: Chickpea is the third major pulse produced globally, with 11.6 million tonnes produced per annum (Merga and Haji, 2019).&nbsp;Sugar alcohols, inulin, starch are all prebiotic carbohydrates found in chickpeas (Johnson et al., 2020).&nbsp;Near-Infrared (NIR) spectroscopy is a non-destructive, versatile and powerful analytical technique. 
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Methods: Spectral data obtained from NIR spectroscopy requires application of various techniques to extract useful information from spectral data which is further used for building various models for prediction of physical or chemical components presents in agricultural crops. The main aim of this study is to apply various machine learning algorithms especially effective in predicting sugar concentration in chickpea. Sugar prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R2) and Adjusted Coefficient of Determination (adjusted R2). 
 
Result: It was observed that, RF outperformed all other models in terms of accuracy for predicting sugar component from preprocessed spectra, with RMSE, RSE, R2 and adjusted R2 values of 0.054, 0.062, 0.954 and 0.937, respectively. The accuracy of the ANN model is similar to that of the RF, with minor differences in RMSE, RSE, R2 and adjusted R2 , values of 0.057, 0.067, 0.952 and 0.935.</div>

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

<div>Background:&nbsp;Moong bean (Vigna radiate L.) plays an important role in antioxidant, antimicrobial, antidiabetic, anti inflammatory, antihypertensive and antitumor effects. Mostly beans and legumes cause gas and abdominal bloating in some people but the moong beans are the easy to digest. Soaking the beans in water before cooking and also sprouting them reduces this issue. The high levels of amino acids, oligosaccharides and polyphenols provide high antioxidant levels not only fight tumors and cancer but also protect against DNA damage and cell mutation. The seed of moong bean acts as a nutraceutical because it provides beneficial effects on human health and also helps in the prevention of cardiovascular disease, diabetes, digestive tract diseases, overweight, obesity and cancer etc. The flavonoids vitexin and isovitexin have high free radical scavenging activities which help lower oxidative stress, the cancer causative factor. It is not only lower blood glucose levels, but also reduce total cholesterol levels, triglycerides, plasma C peptides and glucagon levels. Samples of moong bean were collected from Legume Section of the University.&nbsp; 
 
Methods: Through NIRS-2500 evaluated samples of moong bean. It is an instrument through which Near Infrared (NIR) analysis a spectroscopic technique that makes use of the naturally occurring electromagnetic spectrum. This works in the region of the spectrum defined by wavelengths between 700 nm and 2500 nm. All the cultivars were found to cluster in major four groups on the basis of principal component analysis. The result showed the diversity between nutritional and antinutritional factors in the cultivars that could be further used by plant breeders to develop superior genotypes. The moong bean has lots of advantages which cure the several diseases such as cardiovascular disease, type 2 diabetes, digestive diseases and some cancers.&nbsp;&nbsp; 
 
Result: Histidine amino acid was varied from 6.40% to 8.81%, arginine amino acid from 2.21% to 3.87%, valine amino acid from 2.92% to 4.44%, phenylalanine from 9.73% to 13.86%, isoleucine from 9.28% to 10.02%, methionine from 0.09% to 1.26%, leucine from 6.02% to 7.65%, tryptophan from 0.30% to 0.68%, lysine from 2.03% to 2.22% and threonine from 4.32% to 5.56%. The grain color have the direct related to carotenoids and polyphenols. The hardness of the grain is due to fiber amount of the grain. The moisture is related to the properties such as sphericity, porosity, bulk density. Mostly anti-nutrients factors such as phytic acid, tannins, hemagglutinins and polyphenols associated with the grain. The effects of storage and processing on nutrients and food processing properties are needed to enable optimization of processing steps, for better mung bean food quality and process efficiency.</div>

Biochemical Evaluation of Moong Bean (Vigna radiata L.) Characterstics for Nutraceutical Applications

<div>The use of high-quality seeds is one of the most important elements for increasing agricultural production in any farming system. This element has become more crucial than ever for providing enough food security for the rising population, which is expected to exceed nine billion by year 2050. Selecting high yielding varieties of disease, insect, lodging and shattering resistance, along with other desirable characteristics are the basic keys for satisfactory crop performance and yield. The production of high-quality seed is the cornerstone of any successful agriculture program. It is also a good marketing tool for increasing the potential sale of crops, especially in today&rsquo;s competitive market. Therefore, adopting an efficient method to evaluate the seed quality non-destructively is the need of hour. One such technique or method is the use of NIR which helps to assess seed quality non-destructively and sort out seeds based on seed health, seed deterioration, viability, vigour including protein, starch and fatty acid composition as well as abiotic and biotic seed damage. It is a non-destructive analytical technique requires little sample preparation time and high-throughput, which makes it as a seed analysis tool.</div>