Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling.

In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536-3956 cm-1 were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 µg/mL and 0.640 µg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar.

Rapid selection of Andrographis paniculata medicinal plant materials based on major bioactive using near-infrared spectroscopy.

Abstract: The quantitative analysis of andrographolides in Andrographis paniculata plant materials is essential for pharmaceutical factories. This analysis cannot be done for all samples due to the conventional process using the extraction and HPLC methods requires a long analysis time and sample destruction. Therefore, near-infrared spectroscopy (NIRS) was employed to classify the class of A. paniculata and to determine the content of two active ingredients, andrographolide (AP1) and dehydroandrographolide (AP3) in A. paniculata, rapidly and non-destructively. One hundred twenty dried powder samples were obtained from aerial parts, branches, leaves, and branches mixed with leaves. The NIR absorption scans were collected from a broad spectral region (1000-2500 nm). Then, the scanned samples were extracted and analyzed for their AP1 and AP3 contents using an HPLC reference method. The success classification model based on AP1 level was developed using the second derivative pretreated NIR spectra of the entire wavelength region using the Partial Least Squares-Discriminant Analysis (PLS-DA) method. The NIR calibration models were developed and tested for quantitative analysis with 50 independent samples. The models were identified for the analysis of the AP1 content with excellent performance (correlation coefficient (R) = 0.98; standard error of validation (SEV) = 0.24%) and for the analysis of the AP3 content at a good level of efficiency (R = 0.93; SEV = 0.15%). This study showed that NIR spectroscopic method offers rapid analysis for the selection of A. paniculata that meets the requirement in bioactive amount. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-021-01746-0.

Simultaneous Monitoring of the Evolution of Chemical Parameters in the Fermentation Process of Pineapple Fruit Wine Using the Liquid Probe for Near-Infrared Coupled with Chemometrics.

This study used Fourier transform-near-infrared (FT-NIR) spectroscopy equipped with the liquid probe in combination with an efficient wavelength selection method named searching combination moving window partial least squares (SCMWPLS) for the determination of ethanol, total soluble solids, total acidity, and total volatile acid contents in pineapple fruit wine fermentation using Saccharomyces cerevisiae var. burgundy. Two fermentation batches were produced, and the NIR spectral data of the calibration samples in the wavenumber range of 11,536-3952 cm-1 were obtained over ten days of the fermentation period. SCMWPLS coupled with second derivatives searched and optimized spectral intervals containing useful information for building calibration models of four parameters. All models were validated by test samples obtained from an independent fermentation batch. The SCMWPLS models showed better predictions (the lowest value of prediction error and the highest value of residual predictive deviation) with acceptable statistical results (under confidence limits) among the results achieved by using the whole region. The results of this study demonstrated that FT-NIR spectroscopy using a liquid probe coupled with SCMWPLS could select the optimized wavelength regions while reducing spectral points and increasing accuracy for simultaneously monitoring the evolution of four chemical parameters in pineapple fruit wine fermentation.

Feasibility of near-infrared spectroscopy to detect and to quantify adulterants in cow milk.

Cow milk adulteration involves the dilution of milk with a less-expensive component, such as water or whey. Near-infrared spectroscopy (NIRS) was employed to detect the adulterations of milk, non-destructively. Two adulteration types of cow milk with water and whey were prepared, respectively. NIR spectra of milk adulterations and natural milk samples in the region of 1100 - 2500 nm were collected. The classification of milk adulterations and natural milk were conducted by using discriminant partial least squares (DPLS) and soft independent modelling of class analogy (SIMCA) methods. PLS calibration models for the determination of water and whey contents in milk adulteration were also developed, individually. Comparisons of the classification methods, wavelength regions and data pretreatments were investigated, and are reported in this study. This study showed that NIR spectroscopy can be used to detect water or whey adulterants and their contents in milk samples.

Rapid Classification of Turmeric Based on DNA Fingerprint by Near-Infrared Spectroscopy Combined with Moving Window Partial Least Squares-Discrimination Analysis.

In this research, near-infrared (NIR) spectroscopy in combination with moving window partial least squares-discrimination analysis (MWPLS-DA) was utilized to discriminate the variety of turmeric based on DNA markers, which correlated to the quantity of curcuminoid. Curcuminoid was used as a marker compound in variety identification due to the most pharmacological properties of turmeric possessed from it. MWPLS-DA optimized informative NIR spectral regions for the fitting and prediction to {-1/1}-coded turmeric varieties, indicating variables in the development of latent variables in discrimination analysis. Consequently, MWPLS-DA benefited in the selection of combined informative NIR spectral regions of 1100 - 1260, 1300 - 1500 and 1880 - 2500 nm for classification modeling of turmeric variety with 148 calibration samples, and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. An effective and rapid strategy of using NIR in combination with MWPLS-DA provided the best variety identification results of 100% in both specificity and total accuracy for 48 test samples.

Improvement of the partial least squares model performance for oral glucose intake experiments by inside mean centering and inside multiplicative signal correction.

Near infrared (NIR) spectroscopy has become a promising technique for the in vivo monitoring of glucose. Several capillary-rich locations in the body, such as the tongue, forearm, and finger, have been used to collect the in vivo spectra of blood glucose. For such an in vivo determination of blood glucose, collected NIR spectra often show some dependence on the measurement conditions and human body features at the location on which a probe touches. If NIR spectra collected for different oral glucose intake experiments, in which the skin of different patients and the measurement conditions may be quite different, are directly used, partial least squares (PLS) models built by using them would often show a large prediction error because of the differences in the skin of patients and the measurement conditions. In the present study, the NIR spectra in the range of 1300-1900 nm were measured by conveniently touching an optical fiber probe on the forearm skin with a system that was developed for in vivo measurements in our previous work. The spectra were calibrated to resolve the problem derived from the difference of patient skin and the measurement conditions by two proposed methods, inside mean centering and inside multiplicative signal correction (MSC). These two methods are different from the normal mean centering and normal multiplicative signal correction (MSC) that are usually performed to spectra in the calibration set, while inside mean centering and inside MSC are performed to the spectra in every oral glucose intake experiment. With this procedure, spectral variations resulted from the measurement conditions, and human body features will be reduced significantly. More than 3000 NIR spectra were collected during 68 oral glucose intake experiments, and calibrated. The development of PLS calibration models using the spectra show that the prediction errors can be greatly reduced. This is a potential chemometric technique with simplicity, rapidity and efficiency in the pretreatment of NIR spectra collected during oral glucose intake experiments.

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.

Removal of interference signals due to water from in vivo near-infrared (NIR) spectra of blood glucose by region orthogonal signal correction (ROSC).

A novel chemometric method, region orthogonal signal correction (ROSC), is proposed and applied to pretreat near-infrared (NIR) spectra of blood glucose measured in vivo. Water is the most serious interference component in such kinds of noninvasive measurements, because it shows very high absorbance in the spectra. In the present study, the spectra of blood glucose in the range of 1212 - 1889 nm are used, in which the absorption of water around 1440 nm is very high. ROSC aims at removing the interference signal due to water from the spectra by selecting a set of spectra with a special region of 1404 - 1454 nm that mainly contain information about the variation of the interference component, water, and calculating the orthogonal components to the concentrations of glucose that will be removed. The difference between ROSC and orthogonal signal correction (OSC) is that ROSC uses a special region of spectra for the estimation of scores and loading weights of orthogonal components to pretreat the spectra in other regions, while OSC only uses one fixed region of spectra to calculate loadings, scores and weights of OSC components and removes the OSC components in the same region. A clear advantage of ROSC is that it is more interpretable than OSC, because one can select a spectral region to remove the variation of a special component such as water. Another chemometric method, moving window partial least squares (MWPLSR), is also used to select informative regions of glucose from the NIR spectra of blood glucose measured in vivo, leading to improved PLS models. Results of the application of ROSC demonstrate that ROSC-pretreated spectra including the whole spectral region of 1212 - 1889 nm or an informative region of 1600- 1730 nm selected by MWPLSR provide very good performance of the PLS models. Especially, the later region yields a model with RMSECV of 15.8911 mg/dL for four PLS components. ROSC is a potential chemometric technique in the pretreatment of various spectra.

Comparison of performance of partial least squares regression, secured principal component regression, and modified secured principal component regression for determination of human serum albumin, gamma-globulin and glucose in buffer solutions and in vivo blood glucose quantification by near-infrared spectroscopy.

The performances of three multivariate analysis methods--partial least squares (PLS) regression, secured principal component regression (sPCR) and modified secured principal component regression (msPCR)--are compared and tested for the determination of human serum albumin (HSA), gamma-globulin, and glucose in phosphate buffer solutions and blood glucose quantification by near-infrared (NIR) spectroscopy. Results from the application of PLS, sPCR and msPCR are presented, showing that the three methods can determine the concentrations of HSA, gamma-globulin and glucose in phosphate buffer solutions almost equally well provided that the prediction samples contain the same spectral information as the calibration samples. On the other hand, when some potential spectral features appear in new measurements, sPCR and msPCR outperform PLS significantly. The reason for this is that such spectral features are not included during calibration, which leads to a degradation in PLS prediction performance, while sPCR and msPCR can improve their predictions for the concentrations of the analytes by removing the uncalibrated features from the original spectra. This point is demonstrated by successfully applying sPCR and msPCR to in vivo blood glucose measurements. This work therefore shows that sPCR and msPCR may provide possible alternatives to PLS in cases where some uncalibrated spectral features are present in measurements used for concentration prediction.

Moving window cross validation: a new cross validation method for the selection of a rational number of components in a partial least squares calibration model.

A new cross validation method called moving window cross validation (MWCV) is proposed in this study, as a novel method for selecting the rational number of components for building an efficient calibration model in analytical chemistry. This method works with an innovative pattern to split a validation set by a number of given windows that move synchronously along proper subsets of all the samples. Calculations for the mean value of all mean squares error in cross validations (MSECVs) for all splitting forms are made for different numbers of components, and then the optimal number of components for the model can be selected. Performance of MWCV is compared with that of two cross validation methods, leave-one-out cross validation (LOOCV) and Monte Carlo cross validation (MCCV), for partial least squares (PLS) models developed on one simulated data set and two real near-infrared (NIR) spectral data sets. The results reveal that MWCV can avoid a tendency to over-fit the data. Selection of the optimal number of components can be easily made by MWCV because it yields a global minimum in root MSECV at the optimal number of components. Changes in the window size and window number of MWCV do not greatly influence the selection of the number of components. MWCV is demonstrated to be an effective, simple and accurate cross validation method.

Determination of total nitrogen content, pH, density, refractive index, and brix in Thai fish sauces and their classification by near-infrared spectroscopy with searching combination moving window partial least squares.

Near-infrared (NIR) transflectance spectra in the region of 1100-2500 nm were measured for 100 Thai fish sauces. Quantitative analyses of total nitrogen (TN) content, pH, refractive index, density and brix in the Thai fish sauces and their qualitative analyses were carried out by multivariate analyses with the aid of wavelength interval selection method named searching combination moving window partial least squares (SCMWPLS). The optimized informative region for TN selected by SCMWPLS was the region of 2264-2428 nm. A PLS calibration model, which used this region, yielded the lowest root mean square error of prediction (RMSEP) of 0.100% w/v for the PLS factor of 5. This prediction result is significantly better than those obtained by using the whole spectral region or informative regions selected by moving window partial least squares regression (MWPLSR). As for pH, density, refractive index and brix, the 1698-1722, and 2222-2258 nm regions, the 1358-1438 nm region, the 1774-1846, and 2078-2114 nm regions, and the 1322-1442, and 2000-2076 nm regions were selected by SCMWPLS as the optimized regions. The best prediction results were always obtained by use of the optimized regions selected by SCMWPLS. The lowest RMSEP for pH, density, refractive index and brix were 0.170, 0.007 g cm(-3), 0.0079 and 0.435 degrees Brix, respectively. Qualitative models were developed by using four supervised pattern recognitions, linear discriminant analysis (LDA), factor analysis-linear discriminant analysis (FA-LDA), soft independent modeling of class analog (SIMCA), and K neareat neighbors (KNN) for the optimized combination of informative regions of the NIR spectra of fish sauces to classify fish sauces into three groups based on TN. All the developed models can potentially classify the fish sauces with the correct classification rate of more than 82%, and the KNN classified model has the highest correct classification rate (95%). The present study has demonstrated that NIR spectroscopy combined with SCMWPLS is powerful for both the quantitative and qualitative analyses of Thai fish sauces.

Simultaneous determination of human serum albumin, gamma-globulin, and glucose in a phosphate buffer solution by near-infrared spectroscopy with moving window partial least-squares regression.

Near-infrared (NIR) spectra in the 12,000-4,000 cm(-1) region were measured for phosphate buffer solutions containing human serum albumin (HSA), gamma-globulin, and glucose with various concentrations at 37 degrees C. Five levels of full factorial design were used to prepare a sample set consisting of 125 samples of three component mixtures. The concentration ranges of HSA, gamma-globulin and glucose were 0.00-6.00 g dl(-1), 0.00-4.00 g dl(-1) and 0.00-2.00 g dl(-1), respectively. The 125 sample data were split into two sets, the calibration set with 95 data and the prediction set with 30 data. The most informative spectral ranges of 4648-4323, 4647-4255 and 4912-4304 cm(-1) were selected by moving window partial least-squares regression (MWPLSR) for HSA, [gamma]-globulin, and glucose in the mixtures, respectively. For HSA, the correlation coefficient (R) of 0.9998 and the root mean square error of prediction (RMSEP) of 0.0289 g dl(-1) were obtained. For [gamma]-globulin, R of 0.9997 and RMSEP of 0.0252 g dl(-1) were obtained. The corresponding statistic values of glucose were 0.9997 and 0.0156 g dl(-1), respectively. These statistical values obtained by MWPLSR are highly significant and better than those calculated by using the regions reported in the literature. The results presented here show that MWPLSR can select the informative regions with a simple procedure and increase the power of NIR spectroscopy for simultaneous determination of the concentrations of HSA, [gamma]-globulin and glucose in the mixture systems.