Effective detection of tomatoes with early decay still remains one of the major problems in the post-harvest processing. The feasibility of using visible and near infrared (Vis-NIR) hyperspectral reflectance imaging coupled with image-spectrum merging processing and analysis technology to detect early decay on tomatoes was observed. Mean normalization method was used to correct the uneven illumination caused by the large curvature change and the reflective characteristics of tomato surface. Principal component (PC) clustering analysis of spectra was used to find the optimal PC that was then used to distinguish sound and decayed tissues of tomatoes. Based on the selected PC, three characteristic wavelengths images at 596, 666, and 858 nm were obtained and the new combination image was calculated. The pseudo-color image processing and RGB (red, green, and blue) transformation were applied to enhance the contrast between decayed and sound regions on the combined image. G component of RGB image and the corrected single-wavelength image at 666 nm were used for segmentation of the decayed region and stem-end on tomatoes, respectively. All samples including 120 decayed and 120 sound tomatoes were used

Processing tomato (Lycopersicon esculentum Mill.) is a very important horticultural product all over the world. Soluble solid content (SSC) as a key quality assessment parameter of processing tomato directly affects quality and cost of processing tomato products such as tomato paste. This study proposes an analytical method for quantitatively assessment of SSC in processing tomatoes by using the visible and near-infrared (Vis-NIR) hyperspectral transmittance imaging technique and multiparameter compensation models. Monte-Carlo outlier detection method was applied to optimize sample set. The competitive adaptive reweighted sampling (CARS) algorithm was used to identify the optimal wavelengths from transmittance spectra. Two characteristic parameters including area (size) and weight of samples were measured and different models including conventional PLS/LS-SVM models and multiparameter compensation PLS/LS-SVM models were established, respectively. The results obtained by comparing all the established models showed that the multiparameter (spectrum, area, and weight) compensation CARS-LS-SVM model with 47 important wavelengths had the best assessment ability of SSC in processing tomatoes. The prediction accuracies of the optimal model are r cal = 0.95 and RMSEC = 0.16 for calibration set, and r pre = 0.94, RMSEP = 0.17 and RPD = 2.94 for prediction set, respectively. Research results indicated that the newly proposed Vis-NIR hyperspectral transmittance imaging combining with multi-parameter compensation LS-SVM model would be potential as a noninvasive technique to quantitatively evaluate the SSC in processing tomatoes. the acquisition capability of effective spectral information from the tested object.This work was valuable for setting up the fast multispectral inspection system that can be used for SSC measurement of processing tomatoes in practical on-line grading applications.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Large amounts of waste result from licorice mold rot; moreover, prompt drying directly influences product quality and value. This study compared various glycyrrhiza drying methods (Hot air drying (HAD), infrared combined hot air drying (IR-HAD), vacuum freeze drying (VFD), microwave vacuum drying (MVD), and vacuum pulsation drying (VPD)) that are used in the processing of traditional Chinese medicine. To investigate the effects of various drying methods on the drying characteristics and internal quality of licorice slices, their color, browning, total phenol, total flavonoid, and active components (liquiritin and glycyrrhizic acid) were chosen as qualitative and quantitative evaluation indices. Our results revealed that VFD had the longest drying time, but it could effectively maintain the contents of total phenol, total flavonoid, and liquiritin and glycyrrhizic acid. The results also showed that VFD samples had the best color and the lowest degree of browning, followed by HAD, IR-HAD, and VPD. We think that VFD is the best approach to ensure that licorice is dry.