Internal mildewed nutmeg is difficult to perceive without cutting the nutmeg open and examining it carefully, which poses a significant risk to public health. At present, macroscopic identification and chromatographic analysis are applied to determine whether nutmeg is moldy or not. However, the former relies on a human panel, with the disadvantages of subjectivity and empirical dependence, whilst the latter is generally time-consuming and requires organic solvents. Therefore, it is urgent to develop a rapid and feasible approach for evaluating the quality and predicting mildew in nutmeg. In this study, the quality and odor characteristics of five groups of nutmeg samples with different degrees of mildew were analyzed by using the responses of an electronic nose combined with chemical profiling. The main physicochemical indicators, such as the levels of α-pinene, β-pinene, elemicin, and dehydro-di-isoeugenol, were determined. The results revealed that the contents of α-pinene, β-pinene, and elemicin changed significantly with the extension of storage time. Through the use of an electronic nose and HS–GC–MS technology to assess the overall odor characteristics of nutmeg samples, it was found that the production of volatile organic compounds (VOCs) such as ammonia/organic amines, carbon monoxide, ethanol, and hydrogen sulfide, as well as changes in the terpene and phenylpropene components of the nutmeg itself, may be the material basis for the changes in odor. The accuracy of the qualitative classification model for the degree of mildew in nutmeg was higher than 90% according to the electronic nose data combined with different machine learning algorithms. Quantitative models were established for predicting the contents of the chemical components, and models based on a BP neural network (BPNN), the support vector machine (SVM), and the random forest algorithm (RF) all showed good performance in predicting the concentrations of these chemical components, except for dehydro-di-isoeugenol. The BPNN performed effectively in predicting the storage time of nutmeg on the basis of the E-nose’s responses, with an RMSE and R² of 0.268 and 0.996 for the training set, and 0.317 and 0.993 for the testing set, respectively. The results demonstrated that the responses of the electronic nose (E-nose) had a high correlation with the internal quality of nutmeg. This work proposes a quick and non-destructive evaluation method for the quality of nutmeg, which has high accuracy in discriminating between different degrees of mold in nutmeg and is conducive to early detection and warning of moldy phenomena.

Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used for gastrointestinal diseases. With increased demand, there are more and more places of cultivation for AMR. However, the quality of AMR varies from place to place, and there is no good way to distinguish AMR from different origins at present. In this paper, we determined the content of eight chemical components including 60% ethanol extracts, essential oil, polysaccharides, atractylenolides, and atractylone, obtained the color parameters of AMR powder by colorimetry, and odor information was captured by the electronic nose, all of which were combined with machine learning to establish a rapid origin traceability method. The results of the principal component analysis of the chemical components revealed that Zhejiang AMR has a high comprehensive score and overall better quality. The Kruskal–Wallis test demonstrated that there are varying degrees of differences in chemical composition and color parameters across the different origin. However, the accuracy of the classification model is low (less than 80%), making it difficult to distinguish between different origins of AMR. The electronic nose demonstrated excellent classification performance in the traceability of AMR from different origins, with accuracy reaching more than 90% (PLS-DA: 96.88%, BPNN: 96.88%, PSO-SVM: 100%). Overall, this study clarified the quality differences of AMR among different origins, and a rapid and precise method combining machine learning was developed to trace the origin of AMR.

Codonopsis Radix (CR), an important species of “medicine-food homology”, exhibits broad market prospects, underscoring the urgency and importance of research on its quality. This study specifically measured the alcohol-soluble extract and polysaccharide extract of 77 samples from mainstream producing areas of CR, which serve as key fractions for assessing its quality. Additionally, to gain a comprehensive understanding of the sensory characteristics of samples, the study employed electronic tongue technology to obtain sweetness values, used a colorimeter to determine yellowness values, and captured odor fingerprint information through an electronic nose (E-nose). In the data analysis phase, the study compared the accuracy of various regression prediction models, including Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). After comprehensive evaluation, an SVM algorithm was selected due to its superior prediction performance. To further enhance prediction accuracy, the study utilized a Particle Swarm Optimization (PSO) algorithm to optimize the SVM, resulting in a significant improvement in the prediction accuracy of sweetness values. In conclusion, regression prediction models for chemical composition and sensory information of CR based on an E-nose were established. It represents an enhancement of traditional morphological identification methods for Chinese medicinal herbs and provides new ideas and means for quality evaluation of CR. Furthermore, it offers a reference for quality evaluation of other similar Chinese medicinal herbs.

Ziziphi Spinosae Semen (ZSS), a medicinal and edible homologous herbal drug, is commercially available in both raw and fried (FZSS) forms and has been widely used to improve sleep quality. This study aimed to elucidate the differences in chemical composition between the two specifications. A comprehensive metabolomics approach utilizing ¹H NMR, GC–MS, and UHPLC-Q-Orbitrap-HRMS identified a total of 66 potential biomarkers. The results demonstrated that after frying, the content of fatty acids decreased significantly, while the levels of most primary metabolites (e.g., sugars, amino acids) and secondary metabolites (e.g., alkaloids, flavonoids) increased markedly. Targeted quantification of 14 key components validated these trends: the contents of five fatty acids decreased (p < 0.001), whereas the levels of five secondary metabolites (coclaurine, magnoflorine, spinosin, 6‴-feruloylspinosin, and jujuboside A) increased. In contrast, the content of jujuboside B decreased significantly. This study systematically reveals the profound impact of frying on the chemical composition of ZSS, providing a scientific basis for its quality control and processing optimization.

The rapid detection and quantification of microbial quantity and aflatoxin are crucial for food safety and quality. In order to achieve rapid detection, nutmeg with mildew, but with difficult-to-observe mildew characteristics, was selected as the research object. Its intrinsic component (dehydrodiisoeugenol) and exogenous noxious substances (the total number of colonies and aflatoxin B₁) were determined to clarify their changes during the mold process. Subsequently, electronic nose (E-nose) was employed to analyze the odor of nutmeg and was combined with six machine learning algorithms to establish a classification model for samples with different degrees of mold. Finally, three algorithms were chosen as the preferred options to establish the prediction models of indicator content, which can not only identify whether nutmeg is edible but also measure each index. The results demonstrate the enormous potential of E-nose for real-time detection for assessing food safety. In terms of qualitative analysis, the established classification model can achieve a more than 90% true positive rate, suggesting that E-nose could identify early mildew. In quantitative analysis, E-nose combined with Back Propagation Neural Network achieved the highest prediction accuracy, since the correlation coefficient between the predicted value and the measured value of aflatoxin B₁ is 0.9776, the TAMC is 0.9443, and the TYMC is 0.9685. This study provides a reference for the rapid and comprehensive quality evaluation of mildew-prone nutmeg, and it confirms that E-nose can be applied as a quick and simple technology.

This study aimed to develop an intelligent quality assessment system for Codonopsis Radix based on machine learning modeling. First, Codonopsis Radix samples from six origins were grouped based on pharmacological and chemical indicators, integrating pharmacodynamic evaluations using impaired spleen and lung function animal models with compositional analysis of the alcohol-soluble extract and polysaccharide contents. Subsequently, an electronic nose was employed to objectively quantify their odor profiles. A machine learning-based modeling framework was constructed by integrating feature extraction, feature selection, and pattern recognition techniques. The classification model built by combining electronic nose data with machine learning algorithms demonstrated highly effective discriminatory capability in cross-validation. SHapley Additive exPlanations analysis identified sensors S8, S15, S16, and S18 as critical variables for classification. Concurrently, regression models were established to predict the alcohol-soluble extract and polysaccharide contents. Given the limited sample size, feature expansion and data augmentation strategies were applied exclusively to the training set to enhance model robustness. In summary, the proposed interpretable modeling approach, which integrates pharmacological efficacy, chemical composition, and electronic nose data, provides a referential technical pathway for similar studies.