Quantification of Total Sugars in Kombucha Using Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy and Partial Least Squares (PLS) Regression: A Rapid Alternative to Colorimetric Methods

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Following the recent success in quantitative analysis of essential fatty acid compositions in a commercial microencapsulated fish oil (μEFO) supplement, we extended the application of portable attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis for rapid determination of total protein contents—the other major component in most commercial μEFO powders. In contrast to the traditional chromatographic methodology used in a routine amino acid analysis (AAA), the ATR-FTIR spectra of the μEFO powder can be acquired directly from its original powder form with no requirement of any sample preparation, making the technique exceptionally fast, noninvasive, and environmentally friendly as well as being cost effective and hence eminently suitable for routine use by industry. By optimizing the spectral region of interest and number of latent factors through the developed PLSR strategy, a good linear calibration model was produced as indicated by an excellent value of coefficient of determination R 2 = 0.9975, using standard μEFO powders with total protein contents in the range of 140–450 mg/g. The prediction of the protein contents acquired from an independent validation set through the optimized PLSR model was highly accurate as evidenced through (1) a good linear fitting (R 2 = 0.9759) in the plot of predicted versus reference values, which were obtained from a standard AAA method, (2) lowest root mean square error of prediction (11.64 mg/g), and (3) high residual predictive deviation (6.83) ranked in very good level of predictive quality indicating high robustness and good predictive performance of the achieved PLSR calibration model. The study therefore demonstrated the potential application of the portable ATR-FTIR technique when used together with PLSR analysis for rapid online monitoring of the two major components (i.e., oil and protein contents) in finished μEFO powders in the actual manufacturing setting.

Melon (Cucumis melo L.) is a high-value agricultural commodity known worldwide for its sweet taste and crisp flesh texture, which are important factors for quality and consumer acceptance. Unfortunately, quality testing and determining the optimal harvest time for achieving desired melon characteristics are traditionally performed through destructive methods. The aim of this study was to explore the potential of acoustic and ultrasonic tests for predicting the physicochemical properties of Honey Globe melons (Cucumis melo L. var. inodorus). A total of 100 melon samples were used in this study. For the nondestructive ultrasonic testing, attenuation values served as its variable, whereas in acoustic testing, variables included frequency, magnitude, short-term energy, and zero-moment. Fruit’s flesh firmness and total soluble solids (TSS) as physicochemical quality properties were determined using destructive tests. The calibration phase involved 80 melon samples, employing a K-Fold Cross Validation approach with ten folds, done on Partial Least Square Regression (PLS) modeling. Another 20 melon samples were used for blind testing. Reliability evaluation was done on key metrics, consisting of R2 values, RMSEC (Root Mean Square Error of Calibration), RMSECV (Root Mean Square Error of Cross-Validation), RMSEP (Root Mean Square Error of Prediction), and RPD (Ratio of Performance to Deviation). Analysis results on these metrics collectively support the conclusion that both ultrasonic and acoustic methods exhibit their potential to predict the firmness properties of melon fruits. The best evaluation result that has been conducted for the ultrasonic test uses attenuation, age, and density as predictors to predict fruit firmness, with R2 = 0.763 and RPD = 2.945, while the acoustic test achieved the best result with magnitude used as a predictor to predict fruit firmness with R2 = 0.718 and RPD = 2.230. However, evaluation metrics on the prediction of total soluble solids for both nondestructive tests were still not good enough for application with low R2 and RPD value.

A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy

Fish oil dietary supplements have been linked to various health benefits due to the high concentration of omega-3 polyunsaturated fatty acid (ω-3 PUFA). The potential use of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with partial least squares regression (PLSR) was assessed to determine the docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and ω-3 PUFA in commercial fish oil capsules taking as a reference 1H NMR spectroscopy values. Comparing the results achieved by interval PLS (iPLS), synergy interval PLS (siPLS), and backward interval PLS (biPLS) algorithms, it was found that siPLS provided the best results. The proposed method predicted DHA with a coefficient of determination (R2) of 0.990, root mean square error of cross-validation (RMSECV) of 0.625%, and root mean square error of prediction (RMSEP) of 1.941. EPA (R2=0.976, RMSECV=0.789%, and RMSEP=2.795%) and ω-3 PUFA (R2=0.978, RMSECV=2.667%, and RMSEP=3.980%). The results indicated that ATR-FTIR and siPLS provided a robust method that could be employed in the analysis and quality control of fish oil supplement capsules. This method has the advantage of being simple, fast, and non-destructive for quantitative analysis.

Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics

The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively. The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.

Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Near-infrared (NIR) spectroscopy is a fast, non-invasive, and effective technique that has gained widespread use in soil analysis. Pre-processing plays an essential role in enhancing the precision of calibrating NIR spectra with laboratory-measured soil properties. This research assessed the efficacy of two two-band index transformations—simple ratio indices (SRI) and normalized difference indices (NDI)—in addition to four distinct three-band index transformations (TBI) for predicting various soil characteristics using NIR spectroscopy in a laboratory setting. A total of 333 soil samples were sourced from different farms across Northern Germany, analyzed using two NIR spectrometers, and their properties were measured in a certified lab. Several feature selection approaches, including recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO), were employed to identify the most significant wavebands. Calibration models were developed using partial least squares regression (PLSR) and LASSO regression. The results indicated that index transformations considerably enhanced the predictive performance of the models. Model performance was assessed through several metrics, including the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD). Compared to unprocessed data, R2 values improved by up to 0.13, 0.30, and 0.23 for organic matter (OM), pH, and phosphorus (P2O5), respectively. The optimal models for estimating OM (R2=0.59, RMSE = 1.61%, RPD = 1.79), pH (R2=0.63, RMSE = 0.28, RPD = 1.73), and P2O5 (R2=0.46, RMSE = 16.1 mg/100 g, RPD = 1.46) were attributed to TBI transformations on selected wavebands, calibrated using PLSR. These findings highlight that NIR spectroscopy, even with a limited spectral range (950–1650 nm), can provide reliable estimates of soil properties when combined with suitable pre-processing methods.

Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

Dendrobium huoshanense (DHS) has long been used to make tea drink, soup, and porridge to protect eye and liver in many Southeast Asian countries for centuries. As a rare and endangered functional food, adulteration in DHS with visually similar but cheaper and more accessible plants such as Dendrobium henanense (DHN) because of their similarities in morphology has become prevalent in the market. In this study, the Attenuated Total Reflectance Fourier transform Infrared Spectroscopy (ATR-FTIR) combined with chemometric methods was established to detect fraudulent addition in DHS with DHN. The partial least squares (PLS) models based on the ATR-FTIR files of DHS mixed with different proportions of DHN were built under cross validation and tested with different independent data sets. To reduce the variables’ lack of information and increase the accuracy of the model, different wavelength selection methods including Moving Window Partial Least Squares (MW-PLS), Monte Carlo-uninformative variable elimination (MC-UVE), and interval random frog (iRF) were compared.The results showed that iRF performed the most perfectly with the number of latent variables (nLVs = 7), the lowest Root Mean Square Error of Cross-Validation (RMSECV = 7.37), and the maximum determination coefficients (R2 = 0.9721). The excellent performance of the model was proved by the low RMSEP value of 6.44% and the high R2 value of 0.9556. The developed method could rapidly quantify the adulteration DHN in DHS, and our study might provide an efficient and great potential technique tool for the rapid, green, low-cost, and nondestructive identification and quantification for DHS adulterated with DHN.

This study aimed to examine the feasibility of evaluating the stress level at the surface of lumber during drying using near-infrared (NIR) spectroscopy combined with artificial neural networks (ANNs). Sugi (Cryptomeria japonica D. Don) lumber with an initial moisture content ranging from 41.1 to 85.8% was dried using a commercial drying schedule. An ANN model for predicting surface-released strain (SRS) was developed based on NIR spectra collected from the lumber during drying. The predictive ability of the ANN model was compared with a partial least squares (PLS) regression model. The ANN model showed good correlation between laboratory-measured SRS and predicted SRS with an R 2 of 0.79, a root mean square error of prediction (RMSEP) of 0.0009, and a ratio of performance to deviation (RPD) of 1.81. The PLS regression model gave a lower R 2 of 0.69, a higher RMSEP of 0.0010, and a lower RPD of 1.38 than the ANN model, suggesting that the predictive performance of the ANN model was superior to the PLS regression model. The SRS evolution during drying as predicted by the models showed a similar trend to the laboratory-measured one. The predicted elapsed times to reach maximum tensile SRS and stress reversal roughly coincided with the laboratory-measured times. These results suggest that NIR spectroscopy combined with multivariate analysis has the potential to predict the drying stress level on the lumber surface and the critical periods during drying, such as the points of maximum tensile stress and stress reversal.

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm-1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.