Background: ATR-FTIR spectroscopy is proved to be a simple, reliable, rapid and easy-to-use technique to assess the groundnut seed quality. Groundnut seeds are highly sensitive to deterioration mainly due to their chemical composition and moisture content. The high oil content and fatty acids of groundnut seeds reduces the seed viability well in advance due to lipid peroxidation process, which is the most frequent cause of seed ageing. As a result, not only lipid peroxidation, but also a series of reactions that produce toxic products occur.&nbsp; 
 
Methods: Eleven groundnut seed lots were tested for their seed germination potential as well as the oil quality parameters, ATR-FTIR analysis, antioxidant enzyme activities and physiological seed quality parameters. 
 
Result: The results indicated that the FTIR spectra of the seed lots having higher germination (&gt;70%) exhibited deviations at different wave numbers within the range of 4000-400 cm-1 when compared with seed lots having lower germination (&lt;70%). The peak intensity was found to follow an increasing trend to a decrease in germination percentage of the seed lots. The seeds lose its viability, the activity of antioxidant enzyme decreases leading to an alteration in free fatty acid, acid value, saponification value, iodine value and peroxide value. Finally, the study concluded that the changes in seed germination from 92 to 12 per cent and their corresponding enzyme activity are closely correlated with FTIR spectral data. Hence, ATR FTIR may be used as a tool to predict the quality of the groundnut seeds during storage.&nbsp;

Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) Spectroscopy: A Tool to Determine Groundnut Seed Quality

<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. 
&nbsp;&nbsp; 
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

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

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

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>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>

<div>Role of Near - Infrared Spectroscopy in Seed Quality Evaluation: A Review</div>

<div>Background: Agrobacterium mediated genetic transformation experiments were carried out in Dolichos bean Cv. (Konkan Bhushan) showing better regenerability.&nbsp; 
 
Methods: Three cry genes viz. cry1Aabc, cry1Fa1 and cry2Aa were used for the transformation each of which were linked to CaMV35S promoter and nptII gene under control of nos promoter and terminator. A vector system consisting of the disarmed hyper virulent Agrobacterium tumefaciens strain EHA-105 harboring pBinAR or BinBt3 was used. Mature embryo axis with single cotyledon was used as explant. Kanamycin as well as PCR screening was carried out to assess the transformation frequency. Progeny analysis using PCR was also carried out to assess the transgene segregation and stable transformation. 
 
Result: Kanamycin concentration of 500 mg/l was found as optimum for selection of a transgenic turning leaf blades albino. Among five methods of colonization used, the method employing mild injury to explant with dipping in Agrobacterium culture for 20 minutes followed by co-cultivation for 48 hours, cefotaxime washing and sowing in soil resulted in maximum survival (74.80%) associated with maximum transformation frequency through PCR analysis (2.13%). Among three cry genes, the gene cry2Aa was found the most effective in transforming Dolichos bean. The progeny analysis of transformants has shown the 3:1 mendelian segregation ratio confirming stable transformation of transgene.</div>

<div>Genetic Transformation for Pod Borer Resistance in Dolichos Bean [Lablab purpureus (L.) Sweet]</div>

The main aim of this study was to determine the role of Infrared lamps in cold stress alleviation during winter in Murrah calves. Ten newborn calves were randomly divided into two groups (G1 and G2) of five each. The calves of G1 were provided with no additional protection; however calves of G2 were protected against cold weather by using Infrared lamps. The body weight (kg) of calves was recorded at weekly interval. The blood samples collected at fortnightly interval were analyzed for Packed Cell volume (PCV, %), hemoglobin (Hb, g/dl), Total Serum Protein (TSP, g/l), albumin (g/l), globulin (g/l) and albumin globulin (A:G) ratio and hormones viz., triiodothyronine (T3, ng/ml), thyroxine (T4, ng/ml) and cortisol (ng/ml). The total body weight gain and average daily gain (ADG) was significantly (P&lt;0.01) higher in G2 as compared to G1. The PCV values were significantly (P&lt;0.05) higher on day 15 and day 45 in G1 than G2. The albumin and A:G Ratio were significantly (P&lt;0.05) higher on day 60 and 45, respectively in G1 than G2. The cortisol levels were higher in G1 than G2 and differences were highly significant (P&lt;0.01) on 15th day and significant (P&lt;0.05) on 45th day. Significantly (P&lt;0.01) higher values of &nbsp;T3 and T4 were observed on 15th and 45th day in G1 as compared to G2. On the basis of the results, it may be concluded that Infrared lamps can be effectively used to protect newborn calves from adverse conditions of winter and to improve their body growth performance. &nbsp;

Role of Infrared lamps in cold stress alleviation during winter in Murrah calves

<div style="text-align: justify;">In planta transformation has been established as an innovative and simple technique involving direct transformation of plant parts without involving the tedious tissue culture step. Methodologies of in planta transformation involve different plant parts and strategies. Agrobacterium strain carrying the gene of interest is targeted to the specific plant part either directly or in the induction medium. Vacuum infiltration is sometimes used to facilitate foreign gene integration. Initial studies of this novel technique involved treatment of whole plants with the inoculum, and later shifted to treatments of shoot tips and floral parts and finally the female reproductive parts have been targeted. Zygotes, embryos and seeds have recently been used extensively yielding successful transformation. The method is simple, convenient and overcomes the problem of tissue culture induced genetic variability in the transformants. The review traces the origin and development in the in planta methodologies used over the past and the various parameters considered by the workers for increased effectiveness viz. developmental stages, Agrobacterium stain, surfactant, induction medium etc., in the various crops compiled. Based on the review it may be inferred that there is immense potential in planta transformation, and the ease of regeneration and selection of transformants in the methods described, can be utilized for crop improvement.</div>

<div style="text-align:justify">In Planta&nbsp;Transformation in Plants: A Review</div>

<div>Background: Common bean (Phaseolus vulgaris L.) is a globally important grain and vegetable legume crop, providing a substantial portion of the diet protein and minerals for many people in the developing world. However, the genetic studies and improvement on this crop has long been impeded by its recalcitrance to Agrobacterium-mediated whole plant genetic transformation. Established Agrobacterium rhizogenes-based hairy root transformation in common bean heavily relies on the strain K599. 
 
Methods: In order to develop an efficient alternative protocol for hair transformation in common bean, the efficiency of Agrobacterium rhizogenes strain R1000 in inducing hairy roots from 6-day-old seedlings with root below cotyledons excised by the soaking and smearing method were tested. The binary plasmid pBI121 with the reporter gene GUS (pBI121-GUS) or eGFP (pBI121-eGFP) driven by the constitutive promoter was used for transformation and rapid identification of the transgenic hairy roots.&nbsp; &nbsp; 
 
Result: We established a strain R1000-based system for the induction of hairy roots in common bean. The plant receptor genotypes and infection methods were optimized, which led to a high transformation rate of hairy roots up to 60%. This method therefore provides a useful alternative means for functional genomic studies in common bean.</div>

Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) Spectroscopy: A Tool to Determine Groundnut Seed Quality

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

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

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

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

Role of Near - Infrared Spectroscopy in Seed Quality Evaluation: A Review

Genetic Transformation for Pod Borer Resistance in Dolichos Bean [Lablab purpureus (L.) Sweet]

Role of Infrared lamps in cold stress alleviation during winter in Murrah calves

In Planta Transformation in Plants: A Review

An Agrobacterium rhizogenes Strain R1000-mediated Efficient Hairy Root Transformation Protocol for Common Bean