Identification of Nutmeg With Different Mildew Degree Based on HPLC Fingerprint, GC-MS, and E-Nose

Abstract EFSA was asked to deliver a scientific opinion on the risks to public health related to the presence of aflatoxins in food. The risk assessment was confined to aflatoxin B1 (AFB1), AFB2, AFG1, AFG2 and AFM1. More than 200,000 analytical results on the occurrence of aflatoxins were used in the evaluation. Grains and grain‐based products made the largest contribution to the mean chronic dietary exposure to AFB1 in all age classes, while ‘liquid milk’ and ‘fermented milk products’ were the main contributors to the AFM1 mean exposure. Aflatoxins are genotoxic and AFB1 can cause hepatocellular carcinomas (HCCs) in humans. The CONTAM Panel selected a benchmark dose lower confidence limit (BMDL) for a benchmark response of 10% of 0.4 μg/kg body weight (bw) per day for the incidence of HCC in male rats following AFB1 exposure to be used in a margin of exposure (MOE) approach. The calculation of a BMDL from the human data was not appropriate; instead, the cancer potencies estimated by the Joint FAO/WHO Expert Committee on Food Additives in 2016 were used. For AFM1, a potency factor of 0.1 relative to AFB1 was used. For AFG1, AFB2 and AFG2, the in vivo data are not sufficient to derive potency factors and equal potency to AFB1 was assumed as in previous assessments. MOE values for AFB1 exposure ranged from 5,000 to 29 and for AFM1 from 100,000 to 508. The calculated MOEs are below 10,000 for AFB1 and also for AFM1 where some surveys, particularly for the younger age groups, have an MOE below 10,000. This raises a health concern. The estimated cancer risks in humans following exposure to AFB1 and AFM1 are in‐line with the conclusion drawn from the MOEs. The conclusions also apply to the combined exposure to all five aflatoxins.

A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique.

Tea is one of the most popular beverage with distinct flavor consumed worldwide. It is of significance to establish evaluation method for tea quality controlling. In this work, electronic nose (E-nose) was applied to assess tea quality grades by detecting the volatile components of tea leaves and tea infusion samples. The "35th s value", "70th s value" and "average differential value" were extracted as features from E-nose responding signals. Three data reduction methods including principle component analysis (PCA), multi-dimensional scaling (MDS) and linear discriminant analysis (LDA) were introduced to improve the efficiency of E-nose analysis. Logistic regression (LR) and support vector machine (SVM) were applied to set up qualitative classification models. The results indicated that LDA outperformed original data, PCA and MDS in both LR and SVM models. SVM had an advantage over LR in developing classification models. The classification accuracy of SVM based on the data processed by LDA for tea infusion samples was 100%. Quantitative analysis was conducted to predict the contents of volatile compounds in tea samples based on E-nose signals. The prediction results of SVM based on the data processed by LDA for linalool (training set: R2 = 0.9523; testing set: R2 = 0.9343), nonanal (training set: R2 = 0.9617; testing set: R2 = 0.8980) and geraniol (training set: R2 = 0.9576; testing set: R2 = 0.9315) were satisfactory. The research manifested the feasibility of E-nose for qualitatively and quantitatively analyzing tea quality grades.