Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R² = 0.9928; testing: R² = 0.9928), SSC (training: R² = 0.9749; testing: R² = 0.9143) and firmness (training: R² = 0.9814; testing: R² = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R² reaching 0.9979, while that of the second derivative was the lowest with an R² of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R² value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering a non-invasive method to classify decay levels. To mitigate data scarcity and improve training model robustness, a Gaussian Mixture Embedded Generative Adversarial Network (GMEGAN) was used to generate synthetic data, resulting in augmented datasets that increased diversity and improved model performance. Several machine learning and deep learning models, including traditional classifiers (SVM, LR, RF, ANN) and advanced neural networks (CNN, ECA-CNN, CAM-CNN, Conditional CNN), were trained and evaluated. Models incorporating feature-optimized channel attention modules (f-CAM, f-ECA) achieved a classification accuracy of up to 90.28%, significantly outperforming traditional machine learning models (72–77%) and standard CNN models (83.33%). The inclusion of GMEGAN-generated datasets further enhanced classification performance, especially for feature-optimized Conditional CNN models, with an observed increase in accuracy of up to 5.55%. A comprehensive evaluation of the GMEGAN-generated data, including feature mapping consistency, data distribution similarity, and quality metrics, demonstrated that the generated data closely resembled real data, thereby effectively enhancing dataset diversity. The proposed approach shows significant potential in improving classification accuracy and robustness for agricultural quality assessment, particularly in predicting the decay levels of potatoes.

As a National Geographical Indication (GI) product, Wenzhou Ougan (WZ) is difficult to distinguish visually from mandarins of other origins. In this study, 34 peel samples and 47 juice samples from WZ and three external origins were analyzed by headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and their VOC fingerprints were compared. A total of 59 and 68 VOC signal variables were extracted from peel and juice samples, respectively; compound assignments were tentative annotations based on retention index and drift time matching with the GC-IMS library. Gallery plots and two-dimensional spectra revealed distinct differences in several VOC signal regions among origins. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and variable importance in projection (VIP) values were used to evaluate grouping trends and screen discriminant VOC signal variables. For WZ-origin samples, the main WZ-high discriminant VOC signal variables in peel included furfuryl alcohol, 3-octanone, pentanal, n-nonanal, ethyl 2-methylbutyrate, butyl hexanoate, alpha-pinene, and 3-methylbutyric acid, while those in juice mainly included beta-ocimene, limonene, furfuryl alcohol, 2-acetylfuran, 2-methylfuran, butyric acid, and pentanoic acid. These findings indicate that integrated VOC fingerprints show potential for rapid geographical discrimination of Wenzhou Ougan, while further independent validation and complementary chemical confirmation are required.

Chinese yam planting requires horizontal placement, consistent apical–basal orientation, controlled spacing, and low-damage handling of slender seed segments. This study developed and field-evaluated an integrated vision-guided Chinese yam planter combining furrow opening, feeding, visual inspection and rejection, orientation adjustment, buffered discharge, spacing regulation, furrow tracking, seed placement, and soil covering. Conventional image processing extracted seed-segment length and end orientation, an EfficientNet-B0–kNN one-class method supported auxiliary visual-anomaly screening, and a YOLO11s-seg model with PID control-enabled furrow tracking. Field tests and a two-factor experiment were conducted under sandy-soil conditions. The prototype achieved 22 seedlings min⁻¹, a spacing coefficient of variation of 8.4%, a miss-seeding rate of 4.5 ± 1.3%, an orientation accuracy of 99%, and a path-tracking RMSE of 2.30 ± 0.18 cm; forward velocity was the dominant factor affecting spacing uniformity. The screening module achieved a 98.6% interception rate on marker-simulated anomalies under controlled imaging conditions, while recognition of natural defects remains unvalidated. The system can reduce reliance on skilled labor, and its modular structure and quantified performance provide practical references for users and equipment manufacturers.