Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

Characterization of two closely related citrus cultivars using UPLC-ESI-MS/MS-based widely targeted metabolomics

Citrus cultivars are widely spread worldwide, and some of them only differ by specific mutations along the genome. It is difficult to distinguish them by traditional morphological identification. To accurately identify such similar cultivars, the subtle differences between them must be detected. In this study, UPLC-ESI-MS/MS-based widely targeted metabolomics analysis was conducted to study the chemical differences between two closely related citrus cultivars, Citrus reticulata ‘DHP’ and C. reticulata ‘BZH’. Totally 352 metabolites including 11 terpenoids, 35 alkaloids, 80 phenolic acids, 25 coumarins, 7 lignans, 184 flavonoids and 10 other compounds were detected and identified; Among them, 15 metabolites are unique to DHP and 16 metabolites are unique to BZH. Hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA) can be used to clearly discriminate between DHP and BZH. 93 metabolites including 36 down-regulated and 57 up-regulated are significantly different in DHP and BZH. They are mainly involved in the biosynthesis of flavonoids, flavones, flavonols, and isoflavonoids. In addition, the relative content levels of flavonoids, alkaloids, and terpenoids are much higher in the peel of DHP than that of BZH, the presence of which may correlate with the quality difference of the peels. The results reported herein indicate that metabolite analysis based on UPLC-ESI-MS/MS is an effective means of identifying cultivars with different genotypes, especially those that cannot be distinguished based on traditional identification methods.

Determination of fenthion in environmental water samples by dispersive liquid–liquid microextraction coupled with spectrofluorimetric and chemometrics methods

In the present study, a simple, rapid and efficient dispersive liquid–liquid microextraction (DLLME) coupled with spectrofluorimetry and chemometrics methods have been proposed for the preconcentration and determination of fenthion in water samples. Box–Behnken design was applied for multivariate optimization of the extraction conditions (sample pH, the volume of dispersive solvent and volume of extraction solvent). Analysis of variance was performed to study the statistical significance of the variables, their interactions and the model. Under the optimum conditions, the calibration graph was linear in the range of 5.0–110.0 ng mL-1 with the detection limit of 1.23 ng mL-1 (3Sb/m). Parallel factor analysis (PARAFAC) and partial least square (PLS) modelling were applied for the multivariate calibration of the spectrofluorimetric data. The orthogonal signal correction (OSC) was applied for preprocessing of data matrices and the prediction results of model, and the analysis results were statistically compared. The accuracy of the methods, evaluated by the root mean square error of prediction (RMSEP) for fenthion by OSC-PARAFAC and OSC-PLS models were 0.37 and 0.78, respectively. The proposed procedure could be successfully applied for the determination of fenthion in water samples.

Performance of PLS, GA-PLS and OSC-GA-PLS Models in QSAR Study of Pyrazolone Derivatives Inhibitory Activity, and Image Processing for New Compounds Designing

Nowadays, Pyrazolone and its derivatives have gained a lot of attention due to their biological and medicinal applications. These compounds have antimicrobial, antifungal and anticancer properties. Therefore, using simple methods to prepare these compounds is important. Pyrazolone is one of the inhibitors of kinase domain containing receptor KDR or VEGFR-2. In this study, Quantitative Structure-Activity Relationship (QSAR) analysis was used to predict the inhibitory activity of new pyrazolone derivatives. Also, Bi-dimensional images were used to calculate pixels for QSAR modeling. Furthermore, the partial least squares (PLS) was used to establish a relationship between IC50-dependent variables and independent variables, i.e., pixels or hidden variables. In addition, Genetic Algorithm (GA) was used in PLS method (GA-PLS) to select the descriptors. In this method, the variables which selected to form the calibration model had negligible errors with acceptable characteristics. Pre-processing methods such as Orthogonal Signal Correction (OSC) were used to provide a suitable input for modeling as well as to improve the results of the GA (OSC-GA-PLS). Furthermore, Root Mean Squared Error of Prediction (RMSEP) was used to assess the performance of the models to predict the pIC50 of the studied compounds, the value of which was obtained equal to 0.30, 0.22, and 0.19 for PLS, GA-PLS and OSC-GA-PLS models, respectively. Finally, the proposed QSAR model was developed with the OSC-GA-PLS method to predict the inhibitory activity of the new compounds.