Synergy Interval Research Articles

Intelligent Monitoring of Post-Processing Characteristics in 3D-Printed Food Products: A Focus on Fermentation Process of Starch-gluten Mixture Using NIR and Multivariate Analysis

The production of three-dimensional (3D)-printed food products requires not only optimal 3D-printing adaptability but also appropriate post-processing characteristics. This study aimed to use near infrared (NIR) spectroscopy to predict the rheological properties of 3D-printed dough, enabling intelligent monitoring of the dough’s fermentation process. Utilizing support vector machine (SVM) classification model, the fermentation stages can be classified as under-fermentation, complete fermentation, and over-fermentation. Employing preprocessing methods with Synergy Interval Partial Least Square-Competitive Adaptive Reweighted Sampling (SIPLS-CARS) algorithm, 27, 39, 23, and 27 key wavelengths were filtered from the raw NIR spectral data, corresponding to the prediction of storage modulus (G'), loss modulus (G''), complex viscosity (η*), and loss factor (tan δ), respectively. Quantitatively, SVM (Support Vector Machine) regression outperformed Partial Least Squares (PLS) with Rc2 values (0.95, 0.94, 0.94) and Rp2 values (0.93, 0.93, 0.94) for G', G'', and η*. NIR spectra-based predictive models demonstrated superior performance compared to rheo-fermentation properties models. In summary, these findings show the potential of NIR spectroscopy as a rapid tool for predicting the fermentation progress of 3D-printed doughs.

Feasibility study on non-destructive detection of microplastic content in flour based on portable Raman spectroscopy system combined with mixed variable selection method

Microplastics, as emerging environmental pollutants, have garnered considerable attention due to their contamination of both the environment and food. Microplastics can infiltrate the human food chain through multiple pathways, potentially posing health risks to humans. Currently, non-destructive testing of microplastics in food is considered challenging. This study aims to investigate the feasibility of employing a portable Raman spectroscopy system for non-destructive detection of microplastic content (polystyrene, PS; polyethylene, PE) in flour. In this study, a portable spectrometer was used to collect flour spectra of different abundances of microplastics. To enhance the predictive performance of the partial least squares (PLS) model, a mixed variable selection strategy that combined the wavelength interval selection method (Synergy interval partial least squares, siPLS) and the wavelength point selection method (Least absolute shrinkage and selection operator, LASSO; Multiple feature-spaces ensemble by least absolute shrinkage and selection operator, MFE-LASSO) was proposed. Four regression models (PLS, siPLS, siPLS-LASSO, siPLS-MFE-LASSO) were developed and compared for detecting PS and PE content in flour. The siPLS-MFE-LASSO model exhibited the best generalization performance in the prediction set, and was considered to have the best generalization performance (PS: RP2 = 0.9889, RMSEP=0.0344 %; PE: RP2 = 0.9878, RMSEP=0.0361 %). In conclusion, this study has demonstrated the potential of using a portable Raman spectrometer in conjunction with a mixed variable selection algorithm for non-destructive detection of PS and PE content in flour, providing more possibilities for non-destructive detection of microplastic content in food.

NIR and MIR spectral feature information fusion strategy for multivariate quantitative analysis of tobacco components

The determination of total nicotine, total sugar, reducing sugar and total nitrogen contents in tobacco is of great significance to tobacco quality evaluation and formulation design. To quickly detect the content of 4 components of tobacco, using near-infrared (NIR) and mid-infrared (MIR) spectral data from 129 solid samples of tobacco powder provided by Shanghai Tobacco Group Co., Ltd., Two NIR-MIR spectral fusion techniques are studied, that is, fusion technology 1 is to establish a model by fusing feature variables after variable selection of each spectrum. The fusion technology 2 is to first fuse the NIR-MIR spectral data and then select the variables to establish the model. Both fusion technologies use successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), backward interval PLS (biPLS), forward interval PLS (fiPLS), synergy interval PLS (siPLS), and interval interaction moving window partial least squares (iMWPLS) algorithms to filter wavelength variables. The results showed that for total nicotine and total sugar, the PLSR model established by fusion technology method 2 combined with iMWPLS algorithm is the best, and its RMSEP decreases from 0.2314 to 1.3225 to 0.0821 and 0.8079 respectively compared with the full spectrum fusion method, which is superior to the single NIR and MIR models and NIR-MIR fusion technology 1. For reducing sugars, the simple full-spectrum fusion model has the best analytical ability and the lowest RMSEP, which is superior to the single NIR-MIR models and all models established by two spectral fusion techniques combined with six wavelength selection algorithms. For total nitrogen, the prediction effect of fusion technology 1 combined with iMWPLS algorithm model was significantly improved compared with single NIR and MIR models and NIR-MIR fusion technology 2, and its RMSEP was 0.0634. The results showed that the two NIR-MIR spectral fusion techniques made full use of the complementary information provided by NIR and MIR spectroscopy, and successfully applied them to the rapid detection of total nicotine, total sugar, reducing sugar and total nitrogen content in tobacco, which provided a new method and idea for the rapid detection of tobacco components.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

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.

An efficient method for tracing the geographic origin of Enshi Yulu fresh tea leaves based on near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry.

Rapid and High-Performance Analysis of Total Nitrogen in Coco-Peat Substrate by Coupling Laser-Induced Breakdown Spectroscopy with Multi-Chemometrics

Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient () and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient () and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

Quantitative analysis of four PAHs in oily sludge by surface-enhanced Raman spectroscopy (SERS) combined with partial least squares regression (PLS) based on a novel nano-silver-silicon coupling substrate

Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid–liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 μg/g, 0.342 μg/g, 0.541 μg/g, and 5.132 μg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

Correction of the Effect of Humic Acids on the Fluorescence Detection of Polycyclic Aromatic Hydrocarbons by Combination Spectroscopy.

Polycyclic aromatic hydrocarbons (PAHs) are widely distributed in soil and water, but fluorescence spectroscopy for PAHs is often interfered with organic matter in the environment. The aim of this paper is to evaluate a correction method using combined spectral technology in an environment where humic acids and PAHs coexist. In the present work, humic acids and benzo[ghi]perylene were analyzed in various concentrations using fluorescence and near-infrared (NIR) spectroscopy from single and mixed samples. The NIR prediction model of humic acids in mixed samples was established based on synergy interval partial least squares, and the standard curve of fluorescence spectra for humic acids was established at 478 nm (characteristic wavelength of benzo[ghi]perylene). The fluorescence intensity of humic acids in the mixed sample was predicted from the content derived from the NIR spectra. The final correction was carried out by their exclusion from the fluorescence of the mixture at the same wavelength. The corrected fluorescence intensity was linearly correlated with the concentration of benzo[ghi]perylene with R2 = 0.8362, while R2 = 0.3538 before correction. These results give a new insight into the calibration modeling of the combined spectral method.

VARIABLE SELECTION METHODS APPLIED IN HTGC DATA TO DETERMINE PHYSICOCHEMICAL PROPERTIES OF CRUDE OILS

High-temperature gas chromatography (HTGC) is an analytical technique employed in the petroleum industry for component separation. By incorporating chemometrics, HTGC data can be effectively utilized to predict various properties of crude oil. However, HTGC chromatograms generate a substantial number of variables, some of which may lack pertinent chemical information. Consequently, employing variable selection methods becomes crucial to reduce the number of variables and enhance the predictive capability of calibration models. In this study, the interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and ordered predictors selection (OPS) methods were applied for variable selection to construct linear regression models. The main objective was to investigate the potential of these methods in predicting eight properties of crude oil: American Petroleum Institute (API) gravity, standardized kinematic viscosity at 50 °C (VISp), flash point (FP), Reid vapor pressure (RVP), micro carbon residue (MCR), saturates (SAT), aromatics (ARO), and polar (POL) content. While all variable selection methods yielded satisfactory results, the OPS-PLS regression models consistently exhibited the best performance in estimating these properties, achieving root mean squared error of prediction (RMSEP) values of 1.244 for API, 0.029 for VISp, 15.356 °C for FP, 0.324 kPa for RVP, 0.629 wt% for MCR, 3.691 wt% for SAT, 2.939 wt% for ARO, and 3.374 wt% for POL. Variable selection demonstrated remarkable effectiveness, significantly improving the accuracy of the models, and allowing for the creation of concise models with a focused set of variables.

Soybean polypeptide content detection based on fusion of spectral and image information

Soybean polypeptide is a protein hydrolysate. It has multiple benefits, such as anticancer, antioxidant, etc. Addressing the limitations of traditional methods, such as time-consuming procedures and high costs, this study proposed a novel approach for the rapid, nondestructive detection of soybean polypeptide content. This approach utilizes the fusion of spectral and image information. First, seven preprocessing methods were applied to the spectral data. The results indicated that the model accuracy was highest after applying the standard normal variate preprocessing method. Second, a combined method, consisting of synergy interval partial least squares and iteratively retains informative variables, was proposed to extract characteristic variables. A two-layer Stacking framework ensemble learning model was constructed to compare the detection effects between single spectral input and fusion of spectral and image information input. The results showed that the fusion input model achieved the best performance. The coefficient of determination (R 2) of prediction of the fusion input model reached 0.952, an improvement of 0.061 compared to single spectral input. And root mean square error of prediction (RMSEP) of the fusion input model reached 1.803 × 10−4, a reduction of 7.43 × 10−5 compared to single spectral input. These results indicate that the fusion of spectral and image information can effectively improve the model’s accuracy, offering valuable technical support for soybean breeding and quality detection.

Quantitative Analysis of Biodiesel Adulterants Using Raman Spectroscopy Combined with Synergy Interval Partial Least Squares (siPLS) Algorithms

Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R2P) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root--mean-squared error of prediction (RMSEP) decreased from 0.1912% (v/v) to 0.1367% (v/v), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels.

Near-Infrared Spectral Analysis for Assessing Germination Rate of Rapeseed Seeds: An Applied Sciences Approach

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, Rc and Rp, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an Rc value of 0.9841 and an Rp value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton.

Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis.

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators.

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp=0.9376) and P/F ratio (Rp=0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Determination of polysaccharide content in shiitake mushroom beverage by NIR spectroscopy combined with machine learning: A comparative analysis

A comprehensive comparative analysis of the performance of near-infrared (NIR) spectroscopy combined with various chemometrics and machine learning methods was performed to quantitatively determine polysaccharide content in shiitake mushroom beverages. First, Savitzky-Golay (SG) and orthogonal signal correction (OSC) methods were used for spectral pretreatment. Then, to simplify the model and enhance the generalization performance, variable selection (VS) methods including selectivity ratio (sRatio), variable importance in projection (VIP), recursive partial least square (rPLS), synergy interval partial least square (si-PLS), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the feature wavelengths. Finally, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) regression algorithms were used for the multivariate quantitative analysis of NIR data. The results show that different combinations of pretreatment, VS and modelling methods lead to different prediction performances. Except for XGBoost, almost all linear and non-linear models showed satisfying prediction performance with their Rp2 > 0.95. Of these, SG-VS-BPNN models exhibited the best performance with their Rp2 ≥ 0.98. Therefore, NIR combined with machine learning can provide an intelligent, nondestructive and rapid quantitation of polysaccharide content in beverages. Data will be made available on request.

Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools

Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs), allowed for the development of prediction models of pasting parameters, such as the breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), from 166 rice samples. The models developed using iPLS and siPLS were characterized, respectively, by the following regression values: BD (R = 0.84; R = 0.88); FV (R = 0.57; R = 0.64); PV (R = 0.85; R = 0.90); ST (R = 0.85; R = 0.88); and TR (R = 0.85; R = 0.84). Meanwhile, ANN was also tested and allowed for a significant improvement in the models, characterized by the following values corresponding to the calibration and testing procedures: BD (Rcal = 0.99; Rtest = 0.70), FV (Rcal = 0.99; Rtest = 0.85), PV (Rcal = 0.99; Rtest = 0.80), ST (Rcal = 0.99; Rtest = 0.76), and TR (Rcal = 0.99; Rtest = 0.72). Each model was characterized by a specific spectral region that presented significative influence in terms of the pasting parameters. The machine learning models developed for these pasting parameters represent a significant tool for rice quality evaluation and will have an important influence on the rice value chain, since breeding programs focus on the evaluation of rice quality.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.