Discrimination of oils and fuels using a portable NIR spectrometer

Detection of adulterated milk using two-dimensional correlation spectroscopy combined with multi-way partial least squares

Application of robust principal component analysis–multivariate adaptive regression splines for the determination of °API gravity in crude oil samples using ATR-FTIR spectroscopy

A nondestructive method for evaluation of tomato quality was proposed to recognize the different ripening stages of tomatoes and to predict their hardness. Computer vision and electronic nose were used, and data fusion technology was used to establish the decision model of multi‐attribute information fusion. Fisher discriminant analysis (FDA) model and support vector classifier (SVC) model for tomato grading were established. Partial least squares (PLS) model and support vector regression (SVR) model were used for predicting the hardness. Comparing with the single detection technique, the combined system which fused computer vision and electronic nose, achieved better results. In the combined system: with respect to the SVC model, classification accuracy was 96.38% for the training set, 94.20% for the prediction set; while to the FDA model, classification accuracy was 93.48% for the training set, 85.51% for the prediction set; to the PLS model, the correlation coefficient of prediction (Rp) was 94.09% and the root mean square error of prediction (RMSEP) was 2.33 N; to the SVR model, the Rp was 95.14% and RMSEP was 0.03 N. It was observed that more robust and better prediction performance on tomato detection were achieved by using fusion information.Practical applicationsComputer vision, electronic nose, data fusion, and pattern recognition were used for the evaluation of tomato ripeness and hardness. Computer vision captured the color information of tomatoes and electronic nose responded to the smell of tomatoes. After data fusion, information became more abundant and the discrimination models also got better performance. It proved the possibility of nondestructive method for the evaluation of tomatoes quality during storage. So it is feasible to apply this method to detect tomato quality in factory.

Rapid discrimination and quantification of kudzu root with its adulterant part using FT-NIR and a machine learning algorithm

Development of novel univariate and multivariate validated chemometric methods for the analysis of dasatinib, sorafenib, and vandetanib in pure form, dosage forms and biological fluids

Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations

Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage

The present study has aimed at providing new insight into short-wave near-infrared (short-wave NIR) spectroscopy of fat of milk powder. To do that, we analyzed NIR spectra in the 800-1025nm region of 350 milk powder samples. Based on the whole short-wave NIR spectra, performances of least-square support vector machine (LS-SVM) and partial least squares (PLS) are good. Determination coefficients for prediction were up than 0.95, and the root mean square error of prediction (RMSEP) are less than 0.5. The loading weights of PLS and regression coefficients of PLS and LS-SVM were used to determine the sensitive wavelengths for fat content of milk powder. Optimal four sensitive wavelengths, namely 900, 928, 990, and 1018nm, were obtained, and the spectra at these wavelengths were used for the content determination. Rp2 of LS-SVM models are up than 0.97, and RMSEP are less than 0.26. Thus these wavelengths would be useful for the development of portable instrument or online applications to discriminate the fat content of milk powder.

Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy

This paper tries to explore the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data. The compounds of the dataset were classified using K‐means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters. For a particular training set, PLS models were developed with a number of components optimized by leave‐one‐out Q2 and then the developed models were validated (externally) using the test set compounds. For each set, PLS model was initially constructed using all descriptors (variables). The variables having least standardized values of regression coefficients were deleted and the next model was developed with a reduced set of variables. These steps were performed several times until further reduction in number of variables did not improve Q2 value. In each case, statistical parameters like predictive R2 (R2pred), squared correlation coefficient between observed and predicted values with (r2) and without ($\rm{ r_0^{\rm{2}} }$) intercept and Root Mean Square Error of Prediction (RMSEP) were calculated from the test set compounds. In case of all ten sets, Q2 values steadily increase on deletion of variables while R2pred values do not show any specific trend. In no case, the highest Q2 and highest R2pred appear in the same trial, i.e., with the same combinations of variables. This suggests that from the viewpoint of external predictability, choice of variables for PLS based on Q2 value may not be optimum. Moreover, a clear separation of r2 and r02 curves in some sets suggests that such models may not be truly predictive in spite of acceptable R2pred values. Another observation is that coefficient of determination R2 for the training set is more immune to changes on deletion of variables than the validation parameters like Q2 and R2pred. Finally, a new parameter rm2 has been suggested to indicate external predictability of QSAR models.

Two chemometric methods were performed for the determination of acetic acid of fruit vinegars using near infrared (NIR) spectroscopy. Three varieties of fruit vinegars were prepared and 135 samples (45 samples for each variety) were selected for the calibration set, whereas 45 samples (15 samples for each variety) for the validation set. Partial least squares (PLS) analysis was the calibration method as well as extraction method for latent variables (LVs). The first eight LVs were employed as the inputs of least squares-support vector machine (LS-SVM) model. Then LS-SVM model with radial basis function (RBF) kernel was applied to build the regression model compared with PLS model. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.994, 0.814 and -0.091 by PLS, whereas 0.997, 0.651 and 0.011 by LS-SVM, respectively. LS-SVM model outperformed PLS model, but both models achieved an excellent prediction precision. The results indicated that NIR spectroscopy combined with chemometrics could be utilized as a high precision and fast way for the determination of acetic acid of fruit vinegars.

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

A method of rapidly determining the total polar compounds (TPCs) in frying oils using attenuated total reflectance‐Fourier transform infrared spectroscopy combined with partial least squares (PLS) regression is developed. Oils of various types and geographic origins are used to ensure that the proposed model is robust. The first derivative spectrum is selected as the spectral processing method. The interval PLS, forward interval PLS, and backward interval PLS algorithms are compared in terms of their performance. A correlation coefficient (R2) of 0.9942, a root mean square error of calibration (RMSEC) of 1.1, a root mean square error of prediction (RMSEP) of 2.30, a residual predictive deviation (RPD) of 4.1, and a limit of detection (LOD) of 1.65% are obtained by the fiPLS33 model with fewer latent variables and a lower spectral interval number. In addition, sub‐models using a single type of oil showed higher performance (R2 0.9957–0.9998, RMSEC 0.12–0.92, RMSEP 0.79–1.58, RPD 4.79–9.64, LOD 0.66–1.26%) than the general model. The TPC models developed are accurate, stable, and adaptable, and they can be used to analyze general frying oil samples quickly, regardless of the oil type, and to analyze samples of specific oil types accurately.Practical applications: The content of TPCs is an important indicator of whether the oil has been overused and whether it will be harmful during the frying process. However, traditional chemical methods are time‐consuming, and they have not been used to determine large‐sized samples. In addition, due to a lack of regional optimization, most studies on determining TPCs with FTIR give unsatisfactory model performance. A general TPC model that incorporates several oil types and regional optimization is expected to improve prediction performance. Therefore, the proposed method represents a rapid and accurate tool for measuring TPCs in edible fats and oils.

The aim of this study was the non-destructive assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) ripening, based on the prediction of internal quality attributes (IQA) by short-wave near-infrared reflectance spectroscopy (SW-NIRS) calibration models. Spectra from fruit of 50 trees located in two different orchards, were acquired on tree using a customized portable visible near-infrared (vis-NIR) system. Partial least squares (PLS) was used to build the various IQA calibration models. The models were tested through internal validation (IV) and external validation (EV). Generally, the IV results were always superior to those of EV: regarding IV, a high regression coefficient (R2) and low root mean square error of prediction (RMSEP) were achieved, revealing a good predictive performance for juice pH (R2 = 0.80; RMSEP = 0.10; SDR = 2.23), soluble solids content (SSC) (R2 = 0.79; RMSEP = 0.75 %; SDR = 2.27), titratable acidity (TA) (R² = 0.73; RMSEP = 0.24 % citric acid; SDR = 1.94) and the maturation index (MI) (R2 = 0.80; RMSEP = 1.38; SDR = 2.2). The best EV predictions were obtained for TA (R2 = 0.69; RMSEP = 0.38 % citric acid; SDR = 1.24), and MI (R2 = 0.69; RMSEP = 2.07; SDR = 1.49). Calibration models for glucose, fructose and sucrose showed medium-coarse predictions for both validation strategies. A detailed investigation of MI models was performed, to understand the causes of their poor EV results. In the context of EV, model updating strategies were explored by using some validation samples to improve the calibration model. The methods of bias correction and spiking were tested, showing a clear improvement in the predictions.

Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted. The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level). Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)range is 0.301-1.519. This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.