Leiqing Pan

Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network

Peaches in cold storage may develop chill damage, as symptomized by deteriorated texture and lack of juice. To examine fruit quality, we established a hyperspectral imaging system to detect cold injury, and an artificial neural network (ANN) model was developed for which eight optimal wavelengths were selected. Between normal and chill-damaged peaches, significant differences in fruit quality parameters and the spectral response to correlating selected wavelengths were observed. Evidencing this relationship, the correlation coefficients between quality parameters and the respective spectral response of eight selected wavelengths were -0.587 to -0.700, 0.393 to 0.552, 0.510 to 0.751, and 0.574 to 0.773. With optimal representative wavelengths as inputs for the ANN model, the overall classification accuracy of chill damage was 95.8% for all cold-stored samples. The ANN prediction models for quality parameters performed well, with correlation coefficients from 0.6979 to 0.9026. This research demonstrates feasibility of hyperspectral reflectance imaging technique for detecting cold injury.

Comparison of Spectral and Image Morphological Analysis for Egg Early Hatching Property Detection Based on Hyperspectral Imaging

The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hyperspectral images were collected for every egg on day 0, 1, 2, 3 and 4 of incubation. After imaging, two analysis methods were developed to extract egg hatching characteristic. Firstly, hyperspectral images of samples were evaluated using Principal Component Analysis (PCA) and only one optimal band with 822 nm was selected for extracting spectral characteristics of hatching egg. Secondly, an image segmentation algorithm was applied to isolate the image morphologic characteristics of hatching egg. To investigate the applicability of spectral and image morphological analysis for detecting egg early hatching properties, Learning Vector Quantization neural network (LVQNN) was employed. The experimental results demonstrated that model using image morphological characteristics could achieve better accuracy and generalization than using spectral characteristic parameters, and the discrimination accuracy for eggs with embryo development were 97% at day 3, 100% at day 4. In addition, the recognition results for eggs with weak embryo development reached 81% at day 3, and 92% at day 4. This study suggested that image morphological analysis was a novel application of hyperspectral imaging technology to detect egg early hatching properties.

A Study on Soluble Solids Content Assessment Using Electronic Nose: Persimmon Fruit Picked on Different Dates

Soluble solids content is an important internal quality attribute in determining fruit maturity and harvesting time. In this study, an electronic nose was used to monitor the soluble solids content based on the change of volatile compounds of persimmon fruit during different picking-dates. Principal component analysis was applied to investigate whether the sensors’ response of the electronic nose was able to distinguish persimmons among different picking dates corresponding to different maturity levels. The loading analysis was used to identify those sensors that contribute most for flavor modeling. The results indicated that the electronic nose could distinguish the different picking dates using principal component analysis. The model testing showed that a support vector machine could achieve better prediction accuracy and generalization than multiple linear regression and back-propagation neural network and the average prediction accuracy, root mean square error, and mean relative error of the soluble solids content. By using support vector machine models were 91.36, 0.71, and 0.58%, respectively, which implied that the electronic nose was effective for soluble solids content prediction of persimmons on the basis of the support vector machine model.

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10−4 and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.

Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches.

Antitumor, Antioxidant, and Nitrite Scavenging Effects of Chinese Water Chestnut (Eleocharis dulcis) Peel Flavonoids

The preparation, quantification, and characterization of flavonoid compounds from Chinese water chestnut peel (CWCP) flavonoid extract and ethyl acetate fraction (EF), n-butanol fraction, and water fraction were studied. Among these, EF showed the maximum free radical levels (IC50 values of 0.36, 0.40, and 0.37 mg/mL for DPPH•, ABTS•+ , and •OH, respectively), nitrite scavenging effects (IC50 = 1.89 mg/mL), and A549 cell inhibitory activities (IC50 = 776.12 μg/mL) with the highest value of total flavonoid content (TFC, 421.32 mg/g). Moreover, the contents of 8 flavonoids in this fraction were quantified using high-performance liquid chromatography, and fisetin, diosmetin, luteolin, and tectorigenin were the 4 major flavonoids with levels of 31.66, 29.91, 13.69, and 12.41 mg/g, respectively. Luteolin produced a greater inhibition of human lung cancer A549 cells (IC50 = 59.60 μg/mL) than did fisetin, diosmetin, and tectorigenin. Flow cytometry revealed that the cellular mechanisms of luteolin inhibition of A549 cells were achieved via the induction of cell proliferation arrest at G1 phase and apoptosis/necrosis. Our findings suggest that flavonoids are closely associated with antitumor, antioxidant, and nitrite scavenging effects of CWCP.

Evaluation of lipid oxidation of Chinese-style sausage during processing and storage based on electronic nose

A portable electronic nose was used for extracting flavour fingerprint map of Chinese-style sausage during processing and storage, in parallel with detection of acid value (AV) and peroxide value (POV) for evaluating lipid oxidation. Sausage samples during processing and storage were divided into three and five quality phases, respectively. After comparison of sensors response to lipid oxidation, optimal sensor array was determined. Several classification and regression models were developed to classify samples into their respective quality phase and predict lipid oxidation using full and optimal sensor array. Results indicated classification accuracy for sausage samples were, respectively, above 95% and 82% during the processing and storage. For support vector machine (SVM) and artificial neural networks (ANN) regression models, good performance in predicting AV and POV were obtained, with the coefficients of determination (R2s) >0.914 and 0.814 during processing and storage, respectively. Thus, E-nose demonstrated acceptable feasibility in evaluating the degree of lipid oxidation of Chinese-style sausage during processing and storage.

Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

Study on antibacterial properties and major bioactive constituents of Chinese water chestnut (Eleocharis dulcis) peels extracts/fractions

Recently, considerable attention has been paid to the biologically active of fruit and vegetable processing wastes. In this study, antibacterial properties and major bioactive constituents of Chinese water chestnut peels (CWCP) extracts and fractions were evaluated. The data displayed that ethyl acetate fractions (EF) from CWCP showed greater antibacterial activity than n-butanol fractions, methanol extracts and water fractions against three common foodborne pathogenic bacteria (Staphylococcusaureus, Escherichia coli and Listeria monocytogenes) assessed by the inhibition zone, minimal inhibition concentration and minimal bactericidal concentration values. In accordance with disc diffusion assay, S. aureus was more susceptible to EF than the other two pathogens. Furthermore, action-mode studies indicated that EF exhibited significant bactericidal activity against S. aureus by degrading and disrupting the cell wall and cytoplasmic membrane, ultimately the leakage of cell contents led to cell death. Finally, high-performance liquid chromatography coupled with photodiode array and electrospray ionization mass spectrometry was used for characterization of bioactive constituents in EF, and ten flavonoids aglycons were identified or tentatively identified. The results suggest that the extracts and fractions from CWCP could be potentially used as a possible food supplement to improve food safety by the control or elimination of foodborne pathogenic bacteria.

Comparison of Benchtop Fourier-Transform (FT) and Portable Grating Scanning Spectrometers for Determination of Total Soluble Solid Contents in Single Grape Berry (Vitis vinifera L.) and Calibration Transfer

Near-infrared (NIR) spectroscopy was applied for the determination of total soluble solid contents (SSC) of single Ruby Seedless grape berries using both benchtop Fourier transform (VECTOR 22/N) and portable grating scanning (SupNIR-1500) spectrometers in this study. The results showed that the best SSC prediction was obtained by VECTOR 22/N in the range of 12,000 to 4000 cm−1 (833–2500 nm) for Ruby Seedless with determination coefficient of prediction (Rp2) of 0.918, root mean squares error of prediction (RMSEP) of 0.758% based on least squares support vector machine (LS-SVM). Calibration transfer was conducted on the same spectral range of two instruments (1000–1800 nm) based on the LS-SVM model. By conducting Kennard-Stone (KS) to divide sample sets, selecting the optimal number of standardization samples and applying Passing-Bablok regression to choose the optimal instrument as the master instrument, a modified calibration transfer method between two spectrometers was developed. When 45 samples were selected for the standardization set, the linear interpolation-piecewise direct standardization (linear interpolation-PDS) performed well for calibration transfer with Rp2 of 0.857 and RMSEP of 1.099% in the spectral region of 1000–1800 nm. And it was proved that re-calculating the standardization samples into master model could improve the performance of calibration transfer in this study. This work indicated that NIR could be used as a rapid and non-destructive method for SSC prediction, and provided a feasibility to solve the transfer difficulty between totally different NIR spectrometers.