Determination Of Protein Content Research Articles

Antioxidant Properties of Freeze Dried Mix Fruits and Vegetables Product ‘Miss Freezy’ Using Lyophilization Method

Background: The proper processing of fruits and vegetable-based food products will improve appearance, taste, bioactivities, and preservation. Freeze-dried (FD) or lyophilization of foods extends shelf life and preserves food's nutritional value and bioactivities. The objective of this study is to compare the antioxidant properties, nutritional value, and vitamins of fresh fruit-vegetables and the combined FD product. Methods: A combination of fruits (mango, papaya, star-fruit, guava, banana, bengkuang), and vegetables (cucumber), called “Miss Freezy” were cleaned with an ozonizer and processed using the lyophilization method. The antioxidant activities measured included 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), 2,2-Diphenyl-1-picrylhydrazyl (DPPH), and Hydrogen Peroxide (H2O2) assay. While the proximate analysis measured was the determination of water content, ash content, fat content, protein and carbohydrate content, and vitamin C. Results: The value of antioxidant activity in fruit-vegetable combination FD products was higher than in fresh fruit. The median inhibition concentration (IC50) of DPPH, ABTS, and H2O2 scavenging activities were guava 48.74 µg/ml, guava 128.39 µg/ml, and banana 155.55 µg/ml, respectively. The results showed that the water content of fresh fruits, and vegetables was greater than FD products and inversely proportional to the level of ash, fat, protein, carbohydrates, and vitamin C lower than FD products. Conclusions: Based on the results of antioxidant activities, proximate analysis and vitamin C of the FD product was higher than fresh fruits and vegetables. The FD fruit-vegetable combination product can be used as an alternative snack product that is practical and healthy.

Comparative analysis of different methods for protein quantification in donated human milk.

Human milk is the best option for feeding newborns, especially premature infants. In the absence of breast milk, milk from a human milk bank can be a suitable alternative. However, the nutritional content of human milk may be insufficient to meet these high requirements and milk fortification is needed. To facilitate the implementation of simpler and faster analyzers in neonatal healthcare facilities, this study focuses on the concordance analysis of two different analyzers, one based on mid-infrared and the other on ultrasound, in comparison to the Bradford method for determining protein concentration in human milk. Mature milk samples from donor mothers were collected and pasteurized at the Human Milk Bank of Barcelona and protein quantification was performed using mid-infrared (MIRIS-HMA), ultrasound (MilkoScope Julie27), and the classical Bradford reference methods. The intraclass correlation coefficient (ICC) with 95% confidence interval and Bland-Altman plots were used to assess the agreement between methods. The mean protein concentration of 142 milk samples calculated using MIRIS-HMA, MilkoScope, and the Bradford assay were 1.38, 1.15, and 1.19 g/100 ml, respectively. The ICC was 0.70 for MIRIS-HMA vs. Bradford and 0.37 for MilkoScope vs. Bradford. MIRIS-HMA obtained a better agreement with the Bradford technique and is a promising method for developing new devices based on MIR transmission spectroscopy principles. This study confirms how MIRIS-HMA can be used to accurately calculate the protein concentration of human milk.

ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy

Lablab bean (Lablab purpureus L.), known for its higher protein content provides a promising alternative to reduce reliance on animal-based proteins and support sustainable agriculture. Nowadays, traditional methods for nutritional profiling have been outperformed by Convolutional Neural Networks (CNNs) integrated with Near-Infrared Reflectance Spectroscopy (NIRS). However, an advanced technique called Transformers, despite their potential, remains underexplored for prediction of key quality traits of crops. Thus, in the present study, we aimed to predict lablab bean protein content using a Transformer-based model (termed ProTformer) coupled with NIRS. The Dumas combustion method was used to determine protein content from 112 lablab bean genotypes. The performance of this model was compared with 1D CNN and Modified Partial Least Squares (MPLS)-based models. These models were evaluated using the Coefficient of Determination (RSQ), Residual Prediction Deviation (RPD), bias, and Corrected Standard Error of Prediction (SEP(C)). Our findings show that the Transformer-based model achieved the highest predictive accuracy (RSQ = 0.977, RPD = 13.92), surpassing the 1D CNN (RSQ = 0.956, RPD = 11.33) and MPLS (RSQ = 0.943, RPD = 10.53)- based models by 22.86% and 32.21% in RPD, respectively. The multi-head self-attention mechanism present in the Transformer-based model enables it to concurrently focus on different spectral regions, resulting in superior predictive performance. This study shows the potential of using Transformer-based models coupled with NIRS for nutritional profiling which could be effectively adapted for other crops for rapid screening of large germplasm available at global repositories.

Loss of Lipooligosaccharide Synthesis in Acinetobacter baumannii Produces Changes in Outer Membrane Vesicle Protein Content.

Outer membrane vesicles (OMVs) are nanostructures derived from the outer membrane of Gram-negative bacteria. We previously demonstrated that vaccination with endotoxin-free OMVs isolated from an Acinetobacter baumannii strain lacking lipooligosaccharide (LOS) biosynthesis, due to a mutation in lpxD, provides full protection in a murine sepsis model. The present study characterizes the protein content of highly-purified OMVs isolated from LOS-replete and LOS-deficient strains. Four purification methods were evaluated to obtain highly purified OMV preparations: ultracentrifugation, size exclusion chromatography (SEC), ultracentrifugation followed by SEC, and Optiprep™. OMVs from each method were characterized using nanoparticle tracking analysis and electron microscopy. OMVs from LOS-deficient and LOS-replete strains purified using the Optiprep™ method were subjected to LC-MS/MS analysis to determine protein content. Significant differences in protein composition between OMVs from LOS-deficient and LOS-replete strains were found. Computational analyses using Bepipred 3.0 and SEMA 2.0 indicated that the lack of LOS led to the overexpression of immunogenic proteins found in LOS-containing OMVs and the presence of immune-stimulating proteins absent in LOS-replete OMVs. These findings have important implications for developing OMV-based vaccines against A. baumannii, using both LOS-containing and LOS-free OMVs preparations.

Innovative strategies for protein content determination in dried laver (Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900–1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp2) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.

Proteomic analysis of carbapenem-resistant Klebsiella pneumoniae outer membrane vesicles under the action of phages combined with tigecycline

BackgroundKlebsiella pneumoniae is the most commonly encountered pathogen in clinical practice. Widespread use of broad-spectrum antibiotics has led to the current global dissemination of carbapenem-resistant K. pneumoniae, which poses a significant threat to antibacterial treatment efficacy and public health. Outer membrane vesicles (OMVs) have been identified as carriers capable of facilitating the transfer of virulence and resistance genes. However, the role of OMVs in carbapenem-resistant K. pneumoniae under external pressures such as antibiotic and phage treatments remains unclear.MethodsTo isolate and purify OMVs under the pressure of phages and tigecycline, we subjected K. pneumoniae 0692 harboring plasmid-mediated blaNDM-1 and blaKPC-2 genes to density gradient separation. The double-layer plate method was used to isolate MJ1, which efficiently lysed K. pneumoniae 0692 cells. Transmission electron microscopy (TEM) was used to characterize the isolated phages and extract OMV groups for relevant morphological identification. Determination of protein content of each OMV group was conducted through bicinchoninic acid assay (BCA) and proteomic analysis.ResultsK. pneumoniae 0692 released OMVs in response to different environmental stimuli, which were characterized through TEM as having the typical structure and particle size of OMVs. Phage or tigecycline treatment alone resulted in a slight increase in the mean protein concentration of OMVs secreted by K. pneumoniae 0692 compared to that in the untreated group. However, when phage treatment was combined with tigecycline, there was a significant reduction in the average protein concentration of OMVs compared to tigecycline treatment alone. Proteomics showed that OMVs encapsulated numerous functional proteins and that under different external stresses of phages and tigecycline, the proteins carried by K. pneumoniae 0692-derived OMVs were significantly upregulated or downregulated compared with those in the untreated group.ConclusionsThis study confirmed the ability of OMVs to carry abundant proteins and highlighted the important role of OMV-associated proteins in bacterial responses to phages and tigecycline, representing an important advancement in microbial resistance research.

Determination of total protein and sugar content in soy-based beverages using variable selection methods applied to ATR-FTIR spectroscopy

This study explores the potential of using ATR-FTIR coupled with partial least squares (PLS) and random forest (RF) for predicting total protein (TP), total sugar (TS), reducing sugars (RS), and non-reducing sugars (NRS) in soy-based beverages (SBBs). Employing variable selection techniques such as interval PLS (iPLS), synergy interval PLS (siPLS), uninformative variable elimination (UVE), ordered predictors selection (OPS), bat algorithm (BA), genetic algorithm (GA), and particle swarm optimization (PSO). The OPS-PLS emerges as the optimal model for TS prediction, yielding an RMSEP of 0.114 wt% and R2p of 0.934. GA-PLS excels in TP, RS, and NRS prediction, achieving RMSEP values of 0.024 wt%, 0.273 wt%, and 0.339 wt%, with corresponding R2p values of 0.880, 0.847, and 0.450. In RF modeling, GA-RF is identified as the superior model for all properties. These findings underscore the potential of regression models and ATR-FTIR as effective and environmentally friendly tools for analyzing SBB composition.

Low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone colorimetric screening assay for water-soluble protein

An efficient and rapid method for the detection of total soluble protein in tobacco leaves, utilizing a smartphone-based colorimetric approach has been developed. The proposed low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone colorimetric screening assay integrates commercially available microplates, enabling on-site, high-throughput screening of tobacco leaf quality. The study involves preparing protein standard solutions and constructing standard curves using both spectrophotometric and smartphone-based methods. The LIGHt smartphone colorimetry yielded an average relative standard deviation of 10.6 %, a limit of detection of 2 μg/mL, and an average recovery of 93 %. The results demonstrated a comparable performance between intensities from the blue channel and the absorbance values in reflecting protein concentrations, validating the feasibility of utilizing smartphone colorimetry for protein concentration determination. Our approach demonstrates the potential for practical implementation in the field, providing a cost-effective and user-friendly solution for rapid quality assessment in the tobacco industry. The LIGHt smartphone colorimetry enhances quality control practices in the tobacco sector and offers a promising tool for on-site production quality testing in various industries, such as fruits and vegetables.

Comparative analysis of modified partial least squares regression and hybrid deep learning models for predicting protein content in Perilla (Perilla frutescens L.) seed meal using NIR spectroscopy

Perilla seed meal (PSM), a byproduct of oil extraction from Perilla frutescens L. seeds, is rich in protein (24.26–42.85%) and holds potential as an economical and sustainable animal feed. Traditional methods for assessing protein content are labor-intensive and costly. This study explores Near-Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise, and non-destructive determination of PSM protein content in 126 samples. We developed and evaluated Modified Partial Least Squares (MPLS) regression and deep learning (DL) models, including 1D-CNN (Convolutional Neural Network), LSTM Long Short-Term Memory), and hybrid architectures incorporating skip connections, inception modules, and spectral derivatives. Model performance was validated externally using parameters such as RSQexternal (R-squared), bias, SEP(C) (Standard Error of Prediction), RPD (Residual Prediction Deviation), slope, SD (Standard Deviation), p-value (≥0.05), and the correlation between reference and predicted values. The 1D CNN-LSTM-Inception derivative 1 model achieved the best performance (RPD: 8.0, RSQexternal: 0.98), followed by the MPLS-based model (RPD: 4.88, RSQexternal: 0.96) and the 1D CNN derivative 1 model (RPD: 3.07, RSQexternal: 0.96). These models provide a reliable and advanced technology for the non-destructive screening of PSM protein content, thus aiding in the rapid identification and selection of superior perilla chemotypes from varied backgrounds.

Total Protein Determination by Recovery Fluorescence of Trinitrotoluene-Quenched Protein-Functionalized Gold Nanoclusters.

Determination of total protein content is very important for clinical, pharmaceutical and food chemistry, and the simplicity and rapidity of the existing total protein content assays should be improved. Here, a novel fluorescent sensor for total protein content measurement was described using near-infrared emitting fluorescent gold nanoclusters, which were prepared with egg whites. Trinitrotoluene served as highly efficient quencher to quench the fluorescence of gold nanoclusters. Interestingly, the trinitrotoluene-protein complex that formed by premixing of trinitrotoluene and protein-containing real samples maintained the fluorescence of gold nanoclusters. These new findings provided a novel mechanism to design a fluorescence sensor for determination of total protein levels in various real samples. With available and low-cost bovine serum albumin as reference, the obtained standard curve for total protein determination indicated a linear range of 0.1-3.5g L- 1 with a detection limit of 0.06g L- 1 (3σ rule) and a correlation efficiency (R2) of 0.9850. The applicability of the proposed sensor was validated by determination of total protein levels in real samples. Comparison with the existing spectrophotometric methods, the proposed sensor is advantageous of simplicity, rapidity, and cost-effectiveness.

Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.

The crude protein (CP) content is an important determining factor for the quality of alfalfa, and its accurate and rapid evaluation is a challenge for the industry. A model was developed by combining Fourier transform infrared spectroscopy (FTIS) and chemometric analysis. Fourier spectra were collected in the range of 4000~400 cm-1. Adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) were used for preprocessing the spectral data; competitive adaptive reweighted sampling (CARS) and the characteristic peaks of CP functional groups and moieties were used for feature selection; partial least squares regression (PLSR) and random forest regression (RFR) were used for quantitative prediction modelling. By comparing the combined prediction results of CP content, the predictive performance of airPLST-cars-PLSR-CV was the best, with an RP2 of 0.99 and an RMSEP of 0.053, which is suitable for establishing a small-sample prediction model. The research results show that the combination of the PLSR model can achieve an accurate prediction of the crude protein content of alfalfa forage, which can provide a reliable and effective new detection method for the crude protein content of alfalfa forage.

Decoupling Protein Concentration and Aggregate Content Using Diffusion and Water NMR.

Protein-based biopharmaceutical drugs, such as monoclonal antibodies, account for the majority of the best-selling drugs globally in recent years. For bioprocesses, key performance indicators are the concentration and aggregate level for the product being produced. In water NMR (wNMR), the use of the water transverse relaxation rate [R2(1H2O)] has been previously used to determine protein concentration and aggregate level; however, it cannot be used to separate between them without using an additional technique. This work shows that it is possible to "decouple" these two key characteristics by recording the water diffusion coefficient [D(1H2O)] in conjunction with R2(1H2O), even in the event of overlap in either D(1H2O) or R2(1H2O). This method is demonstrated on three different systems, following appropriate D(1H2O) or R2(1H2O) calibration data acquisition for a protein of interest. Our method highlights the potential use of benchtop NMR as an at-line process analytical technique.

Evaluation of the functional properties of the protein extract obtained from chickpea flour (Cicer arietnum L.)

Chickpeas are proteins rich in globulins, glutelins and albumins and are increasingly consumed in Brazil. The aim of this study was to evaluate the protein solubility and antimicrobial activity of the soluble proteins obtained from chickpea flour. In this way, its flour was obtained, followed by protein solubility from the determination of soluble protein content and evaluation of the minimum inhibitory concentration (MIC) against bacteria that cause food-borne illnesses Staphylococcus aures, Escherichia coli and Salmonella typhi. The protein extract showed that the proteins have their isoelectric point around pH 7.0, while their solubility is around pH 5.0. The proteins present in the extract had antibacterial activity against S. aureus and Salmonella typhi. It was concluded that at pH 5.0 the proteins are more readily available and can contribute to better biological functionality and that the protein extract has the potential to be used in tests against bacteria of interest to the pharmaceutical industry and agro-industry.

Structure information analysis and relative content determination of protein and chitin from yellow mealworm larvae using Raman spectroscopy

Insect protein extract is one of the high-quality protein sources and is frequently viewed as a potential nutrition alternative. However, a more precise method for protein measurement is still needed due to protein overestimation by the Kjeldahl method due to the presence of a large amount of chitin in insects. Therefore, we demonstrated the monitoring of chitin and protein extracted from yellow mealworm larvae through the information on molecular vibration obtained using Raman spectroscopy and infrared (IR) spectroscopy. The NH vibration at 3475 cm−1 is the characteristic peak of chitin in defatted product observed in the Raman spectra. The nitrogen-to-protein conversion factor in protein extracted from larvae by the Raman method was determined based on the NH vibration and found to be 5.66 ± 0.01. We also compared these experimental data to theoretical Raman and IR spectra and determined the possible reasons for why nitrogen elements in chitin affect the determination of protein content. The method of sequentially removing fat and protein could provide more accurate quantification of protein and chitin. Raman spectroscopy is feasible for various types of insects with high chitin content. Compared with the Kjeldahl method, the Raman method is a faster and more accurate measurement method. Moreover, it provides the content of impurities, purity, and structural information.

Rapid and Non-Destructive Determination of the Protein and Fat Contents in Wheat by Hyperspectral Imaging Combined with AdaBoost-SVR Modeling

Wheat protein and fat contents directly affect the flavor and yield of liquor. This study utilized hyperspectral imaging (HSI) technology along with an integrated learning model to rapidly and non-destructively detect protein and fat contents in wheat. The hyperspectral data of the wheat grains was preprocessed using various methods. The competitive adaptive reweighted sampling (CARS) algorithm, the successive projections algorithm (SPA), and the combination of the two algorithms (CARS-SPA) were used to extract the characteristic wavelengths. Three support vector regression (SVR) models were created to forecast wheat protein and fat contents using full-band spectral data and characteristic wavelengths. These models are gray wolf optimized support vector regression (GWO-SVR), particle swarm optimized support vector regression (PSO-SVR), and support vector regression-based integrated learning (AdaBoost-SVR). Comparing the models showed that the AdaBoost-SVR model, utilizing the Eigen wavelengths extracted through the CARS-SPA and CARS algorithms, stood out as the most accurate in predicting wheat protein and fat contents, with Rp 2 values of 0.9789 and 0.9651, respectively. The study findings indicate that combining HSI with AdaBoost-SVR integrated learning modeling enables rapid, non-destructive determination of wheat protein and fat contents with greater accuracy than using a single machine learning (ML) model.

Unveiling Fungi Armor: Preliminary Study on Fortifying Pisum sativum L. Seeds against Drought with Schizophyllum commune Fries 1815 Polysaccharide Fractions.

Amidst worsening climate change, drought stress imperils global agriculture, jeopardizing crop yields and food security, thereby necessitating the urgent exploration of sustainable methods like biopriming for the harnessing of beneficial microorganisms to bolster plant resilience. Recent research has revealed diverse biological compounds with versatile applications produced by Schizophyllum commune, rendering this fungus as a promising contender for biopriming applications. For the first time, this study aimed to investigate the potential of S. commune exo- (EPSH) and intra-polysaccharides (IPSH) isolated from two strains-Italian (ITA) and Serbian (SRB)-under submerged cultivation to enhance the resilience of Pisum sativum L. seeds through the biopriming technique. Testing of the seed quality for the bioprimed, hydroprimed, and unprimed seeds was conducted using a germination test, under optimal and drought conditions, while characterization of the PSHs included FTIR analysis, microanalysis, and determination of total protein content (TPC). The FTIR spectra of EPSH and IPSH were very similar but revealed the impurities, while microanalysis and TPC confirmed a different presence of proteins in the isolated PSHs. In optimal conditions, the IPSH SRB increased germination energy by 5.50% compared to the control; however, the highest percentage of germination (94.70%) was shown after biopriming with the PSH isolated from the ITA strain. Additionally, all assessed treatments resulted in a boost in seedling growth and biomass accumulation, where the ITA strain demonstrated greater effectiveness in optimal conditions, while the SRB strain showed superiority in drought conditions. The drought tolerance indices increased significantly in response to all examined treatments during the drought, with EPSH ITA (23.00%) and EPSH SRB (24.00%) demonstrating the greatest effects. Results of this preliminary study demonstrate the positive effect of isolated PSH, indicating their potential as biopriming agents and offering insights into novel strategies for agricultural resilience.

Effective removal of Rhodamine 6G (Basic Red-1) reactive dye using waxy corn starch in waste water of textile industry

The textile industry generates effluents containing toxic dyes, which have detrimental effects on the environment, including air, soil, and water contamination. These dyes also pose risks to human health and disrupt aquatic ecosystems. Developing ecological and cost-effective method to address this issue is a major challenge. This study focuses on utilizing waxy cornstarch, which exhibits unique properties such as high biodegradability, environmental compatibility, low sludge volume, small particle size, and high surface area (1.27 m2/g), for removing the reactive dye Rhodamine 6G (Basic Red-1) from textile wastewater. The composition of waxy cornstarch was analyzed, including determination of protein content using the Kjeldahl apparatus, as well as moisture content, ash content, and fine fiber content through experimental methods. The rate of dye removal from wastewater was assessed using a UV-spectrophotometer. The experimental design was selected, and trials were conducted using a glass testing apparatus. Dye removal rates were investigated at different dosages, temperatures, and pH ranges. The highest dye removal efficiency of 83.6% was achieved at a pH of 5, a temperature of 40 °C, and a coagulant dosage of 40 mg/l. These findings highlight the potential of waxy cornstarch as a natural coagulant for effective removal of Rhodamine 6G dye from textile wastewater.

Diversity of fungi species that decompose Ceriops tagal leaf litter on Pulau Sembilan, Langkat Regency

One of the processes in mangrove ecosystems that contributes greatly to water fertility is the decomposition or breakdown of mangrove litter. Fungi, as decomposers in the process of decomposing litter, have an important role in the ecosystem and also play a role in regulating nutrient availability and carbon cycling.This research was conducted to determine the number of species and diversity of fungi found in decomposed C. tagal leaf litter, to measure the rate of decomposition of C. tagal leaf litter and to determine the levels of carbohydrates and proteins during the decomposition process.The research was carried out at the Forest Cultivation Laboratory, Faculty of Forestry, Universitas Sumatera Utara. The sampling location of C. tagal leaf litter was in the mangrove ecosystem on Pulau Sembilan, PangkalanSusu District, Langkat Regency.The determination of carbohydrate and protein content was carried out at the Medan Industrial Research and Standardization Center.The research was conducted from June 2022 to December 2022.The average rate of litter decomposition was obtained using the formula: ln (Xt/X0) = -kt, and analysis of carbohydrate and protein content using the SNI 01-2891-1992 method.The results of this research showed that there were 5 types of fungi found in the decomposition process of C. tagal leaf litter, which were Aspergillus sp. 1, Aspergillus sp. 2, Penicillium sp., Trichoderma sp. and Culvularia sp. The average value of fungal diversity is 1.19, which is included in the medium category. The decomposition rate of C. tagal leaf litter was 7.55 per year. On day 90, the lowest carbohydrate content was 3.62% and the highest protein content was 5.4%.

Classification of soybean groups for grain yield and industrial traits using Vnir-Swir spectroscopy

This research aimed to evaluate the accuracy of machine learning techniques in distinguishing groups soybean genotypes according to grain industrial traits using hyperspectral reflectance of the leaves. A total of 32 soybean genotypes were evaluated and allocated in randomized blocks with four replications. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. The spectral analysis of the leaves was carried out with a hyperspectral sensor providing ranges from 350 to 2500 nm. Once the wavelengths were obtained, they were grouped into averages of representative intervals into bands. At the end of the crop cycle, grain yield was obtained, and subsequently the determination of carbohydrate, oil, and protein content. Initially, the genotypes were subjected to cluster analysis using the k-means algorithm and subsequently, the data was subjected to machine learning analysis, using six models: J48 Decision Trees (J48) and REPTree (DT), Random Forest (RF), Artificial Neural Networks (ANW), Logistic Regression (LR) and Support Vector Machine (SVM). Logistic regression (LR) was used as a reference point as it is a traditional regression algorithm. The clusters formed acted as the output of the models, while for the input of the models, two groups of data were used: the spectral variables (SV) obtained by the sensor (350–2500 nm) and the spectral averages of the bands selected (BS) (350–2200 nm). The use of machine learning techniques presented lower responses than the standard technique used in the work, that is, LR, which presented superiority in the classification of soybean genotypes in terms of industrial traits. The use of wavelengths provided better performance of the algorithms in the classification in relation to selected bands.

Immunoassays and Mass Spectrometry for Determination of Protein Concentrations in Genetically Modified Crops.

Quantifying protein levels in genetically modified (GM) crops is crucial in every phase of development, deregulation, and seed production. Immunoassays, particularly enzyme-linked immunosorbent assay, have been the primary protein quantitation techniques for decades within the industry due to their efficiency, adaptability, and credibility. Newer immunoassay technologies like Meso Scale Discovery and Luminex offer enhanced sensitivity and multiplexing capabilities. While mass spectrometry (MS) has been widely used for small molecules and protein detection in the pharmaceutical and agricultural industries (e.g., biomarkers, endogenous allergens), its use in quantifying protein levels in GM crops has been limited. However, as trait portfolios for GM crop have expanded, MS has been increasingly adopted due to its comparable sensitivity, increased specificity, and multiplexing capabilities. This review contrasts the benefits and limitations of immunoassays and MS technologies for protein measurement in GM crops, considering factors such as cost, convenience, and specific analytical needs. Ultimately, both techniques are suitable for assessing protein concentrations in GM crops, with MS offering complementary capabilities to immunoassays. This comparison aims to provide insights into selecting between these techniques based on the user's end point needs.