Kang Tu

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.

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.

Effect of Infrared and Conventional Drying Methods on Physicochemical Characteristics of Stored White Rice

The objective of this study was to investigate the effect of infrared (IR) drying followed by tempering and natural cooling on the change of physicochemical characteristics of white rice during up to 10 months of storage. The physicochemical characteristics of IR-dried rice were also compared with those of conventionally dried rice. It took only 58 s to heat the rough rice from room temperature to 60°C with IR, and 2.17 percentage points of moisture was removed. After four months of storage, the increases in yellowness index, water uptake ratio, and volume expansion ratio of the rice dried with IR were 73.8, 63.9, and 55.3% those of rice dried with an ambient air drying method, respectively. IR drying slightly decreased the gelatinization temperature, enthalpy, and viscosities, reduced the changes in microstructure, and maintained cooking characteristics during storage. Therefore, the IR drying process is recommended to maintain the quality of white rice during storage.

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.

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.

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.

Predicting the growth situation of Pseudomonas aeruginosa on agar plates and meat stuffs using gas sensors

A rapid method of predicting the growing situation of Pseudomonas aeruginosa is presented. Gas sensors were used to acquire volatile compounds generated by P. aeruginosa on agar plates and meat stuffs. Then, optimal sensors were selected to simulate P. aeruginosa growth using modified Logistic and Gompertz equations by odor changes. The results showed that the responses of S8 or S10 yielded high coefficients of determination (R2) of 0.89–0.99 and low root mean square errors (RMSE) of 0.06–0.17 for P. aeruginosa growth, fitting the models on the agar plate. The responses of S9, S4 and the first principal component of 10 sensors fit well with the growth of P. aeruginosa inoculated in meat stored at 4 °C and 20 °C, with R2 of 0.73–0.96 and RMSE of 0.25–1.38. The correlation coefficients between the fitting models, as measured by electronic nose responses, and the colony counts of P. aeruginosa were high, ranging from 0.882 to 0.996 for both plate and meat samples. Also, gas chromatography–mass spectrometry results indicated the presence of specific volatiles of P. aeruginosa on agar plates. This work demonstrated an acceptable feasibility of using gas sensors—a rapid, easy and nondestructive method for predicting P. aeruginosa growth.

Influence of infrared drying on storage characteristics of brown rice

The aim of this study was to improve storage characteristics of brown rice by using infrared radiation drying (IRD) through comparison with hot air drying (HAD) and ambient air drying (AAD). After heating by IR from 20 °C to 60 °C within 58 s, 2.17 percentage points moisture of rough rice (initial moisture content is 25.0 ± 0.2% in dry basis) were removed without adverse effect on germination capacity of husked brown rice. Compared with AAD, IRD slowed down the increase in yellowness, water uptake and volume expansion ratio of brown rice by 47.9%, 41.0% and 37.9% after four months of storage, and decreased the temperature range and enthalpy of gelatinization, the peak and breakdown viscosities. These changes might due to the higher stabilization effect of IRD on the microstructure and thermal properties of proteins and starch granules than AAD. IRD is an effective method to improve storage stability of brown rice.

Hot Air Treatment Induces Disease Resistance through Activating the Phenylpropanoid Metabolism in Cherry Tomato Fruit

To explore the effects of hot air (HA, 38 °C for 12 h) treatment on the phenylpropanoid metabolism in cherry tomatoes, phenylpropanoid metabolite levels and the activities and expression of key enzymes were analyzed in HA-treated fruit. HA treatment enhanced phenylpropanoid metabolism, as evidenced by elevated levels of phenolics and flavonoids, higher activities of phenylalanine ammonia-lyase and cinnamate-4-hydroxylase, and upregulated expression of LeCHS, LeCHI, LeF3H, and LeFLS. Levels of several phenylpropanoid metabolites were higher after HA treatment, including p-coumaric acid, caffeic acid, chlorogenic acid, isoquercitrin, quercetin, and rutin. These metabolic changes may be related to the reduced disease incidence and smaller lesion diameters observed in HA-treated fruit inoculated with Alternaria alternata (black mold) or Botrytis cinerea (gray mold). The results suggest that HA treatment induces disease resistance by activating the phenylpropanoid pathway in cherry tomato fruit.