Vis–NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying

Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

Prediction of moisture content of wood using Modified Random Frog and Vis-NIR hyperspectral imaging

Anthracnose is one of the most relevant diseases of mango crops in producing regions, affecting 60% of production. Currently, its detection is carried out in late stages by human visual inspection. Hyperspectral imaging systems allow the development of non-destructive solutions to inspect and detect internal damage. This work aimed to develop a system for detecting anthracnose in mango fruits using Vis–NIR hyperspectral imaging and discriminant analysis. The usefulness of three-dimensionality reduction methods to minimise redundancy in the spectral data and to obtain a compact number of wavelengths that effectively allow the detection of anthracnose symptoms in mango fruits is also explored. As a result, a classification model based on discriminant analysis and Pearson correlation coefficient was obtained, showing the potential of hyperspectral data to robustly allow the detection of anthracnose symptoms with full or reduced spectra. The findings reported in this study can serve as the basis for developing an anthracnose detection system in mango fruits with multispectral cameras.

Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying

Button mushrooms (Agaricus bisporus) were dried by microwave-vacuum drying (MVD) technique to a moisture content of about 6% (d.b.), and the dried mushrooms were compared with hot-air and freeze-dried (FD) products on the basis of different quality attributes such as color, texture, rehydration ratio and sensory score. Statistical analysis of data revealed significant difference among the drying methods for all the attributes at P ≤ 0.05. Although FD produced the best quality dehydrated products having maximum rehydration ratio, highest instrumental color (L value) and lowest hardness, the MVD mushrooms were rated as equal to FD samples by a sensory panel in terms of appearance, color and overall acceptability. MVD mushrooms had significantly higher rehydration potential, lower density, better color and softer texture than those obtained by air drying (AD). The effect of drying methods on the water sorption properties of dehydrated products was also evaluated at 20, 30 and 40C. It was found that FD products absorbed maximum water vapor and MVD products had a higher sorption capacity than conventional AD products. PRACTICAL APPLICATIONS Microwave-vacuum drying (MVD) has been investigated as a potential method for obtaining high quality dehydrated food products including fruits, vegetables and grains. MVD combines the advantages of both microwave heating and vacuum drying. The low temperature and fast mass transfer provided by vacuum combined with rapid energy transfer by microwave heating generates very rapid, low temperature drying and, thus, it has the potential to improve energy efficiency and product quality. Several researchers have reported microwave assisted drying of foods, where considerable improvements in the drying process and product quality have been found. Good quality dehydrated button mushrooms, having properties comparable to freeze-dried products, can be obtained by MVD method within 30–40 min of drying time. The commercial application of this technology for large-scale drying of mushrooms may be examined for economic viability.

Fresh and ripe mango (Mangifera indica) slices cv Dasheri were dehydrated in a cabinet dryer at drying air temperatures of 50, 60, and 70 oC at a slice thickness of 5 mm. Before dehydration, the mango slices were pre-treated with potassium meta-bisulphite (KMS) at three levels (0.5 %, 1.0 %, and 1.5 % KMS, respectively). Five semi-theoretical and empirical thin-layer drying models (Newton [Lewis], Logarithmic, Demir et al. 2004, Henderson & Pabis, and Wang & Singh) were employed to select the best model that describes the drying process. Moisture diffusivity and activation energy of the mango slices were also evaluated. It was observed that the drying took place in the falling rate period. Demir et al. 2004 and Logarithmic models were found to satisfactorily describe the drying process of the mango slices using R2, x2, SSE, and RMSE as the criteria for selecting the best model. The mango slices' moisture diffusivity and activation energy range from 6.79 to 10.02×10-8 m2/s and 10.03 to 14.73 kJ/mol. Thus, KMS pre-treatment can minimise the mango slices' drying duration and drying cost.

VIS-NIR hyperspectral imaging and multivariate analysis for direct characterization of pelagic fish species

The moisture content (MC) and acidity content (AC) of the fermented grains used in liquor production directly affect the liquor quality and yield; as such, they are important indicators used to evaluate the quality of fermented grains. In this study, extreme gradient enhancement algorithm (XGBoost), partial least square regression (PLSR), and extreme learning machine (ELM) models were developed based on spectral data collected by near-infrared (NIR) hyperspectral imaging (HSI) technology. First, PLSR models were established after SNV and MSC algorithms preprocessed the HSI data, and the best preprocessing method was determined (MC: SNV; AC: MSC). Then, the competitive adaptive reweighting sampling (CARS) algorithm and principal component analysis (PCA), both combined with the successive projection algorithm (SPA), were used to extract the characteristic wavelengths from the full-band spectral data. Ultimately, the XGBoost model developed using the characteristic wavelengths extracted by CARS-SPA most accurately predicted the MC (RPD = 6.4167, RP 2 = 0.9757, RMSEP = 0.0442 g·100 g−1) and AC (RPD = 13.0308, RP 2 = 0.9941, RMSEP = 0.0216 mmol·10 g−1). The results showed that the XGBoost model could more accurately predict the MC and AC of the fermented grains from hyperspectral images of the grains, providing an effective method for the rapid analysis of raw materials used in the fermentation of liquor.

The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.

Spectral imaging including machine vision and multispectral imaging can provide a rapid, nondestructive means to assess agricultural commodities for their quality and safety for human consumption. These nondestructive techniques generally rely on reflectance measurements; the most commonly used spectral regions range through the visible (Vis) to the near infrared (NIR). Another optical sensing technique is based on fluorescence that is generally regarded as more sensitive optical sensing tools compared to reflectance techniques. Recently, we developed two imaging systems for food safety research; a laboratory-based hyperspectral imaging system capable of both reflectance and fluorescence measurements and a multispectral laser induced fluorescence imaging system. Because many contiguous spectral bands are acquired with each hyperspectral image pixel, the resulting data quantity makes uses of hyperspectral imaging spectrometer for on-line food safety inspection impractical. Instead, the spectral and spatial information is used to determine several optimal wavebands that can be implemented to a multispectral imaging system. We present a systematic method for using hyperspectral image data to identify few wavebands to be used in multispectral detection systems. Also presented is a two-band ratio as a multispectral fusion method along with automated detection for animal feces contamination on apples. The fluorescence imaging techniques demonstrated that even minute animal feces, not clearly visible to human eye, could be detected. Practical considerations for development of multispectral fluorescence imaging systems for on-line uses are also discussed.

Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage period, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set, and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh. Keywords: hyperspectral imaging, chicken, TVC, partial least square regression (PLSR) DOI: 10.25165/j.ijabe.20191203.4444 Citation: H Wang, H J He, H J Ma, F S Chen, Z L Kang, M M Zhu, et al. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.

Hyperspectral reflectance imaging technology in near-infrared regions (900–1,700 nm) was used to evaluate soluble solids content (SSC), firmness, moisture content (MC), and pH values of ‘Fuji’ apples during a 13-week storage period. Totally, 167 apples were divided into calibration set (125) and prediction set (42) based on the joint x-y distance sample set partitioning method. Mean spectrum of the regions of interest in the hyperspectral image of each apple was used for analysis. Two typical variable selection methods, i.e., successive projection algorithm (SPA) and uninformative variable elimination (UVE), were applied to extract the characteristic variables from full spectra (FS). The partial least squares (PLS) regression, least squares support vector machine (LSSVM), and backpropagation (BP) network modeling methods were used to establish models to predict SSC, firmness, MC, and pH of apples, respectively. The results showed that the SSC and MC could be predicted exactly by all developed models, and SPA-LSSVM and FS-BP could be used to predict pH value roughly. All models failed to predict firmness. The SPA-LSSVM model had better comprehensive ability in determining SSC, MC, and pH than others, with the correlation coefficient of prediction of 0.961, 0.984, and 0.882 and residual predictive deviation of 3.49, 5.51, and 2.06, respectively. The results demonstrated the feasibility of using near-infrared hyperspectral reflectance imaging technology as a non-invasive method for predicting SSC, MC, and pH of apples simultaneously.

Application of mathematical techniques to study the moisture loss kinetics and polyphenol degradation kinetics of mango (Mangifera indica L.) slices during heat pump drying by pilot equipment

Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380–1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS.

The drying quality of kiwifruit dried by a combination of freeze drying and microwave vacuum drying (FD‐MVD) in terms of drying time, appearance quality and microstructure. The results showed that the quality of FD‐MVD products was highest with FD 12 h because the lowest volume density and close to the color of single FD samples. The optimal microwave power for the MVD process was found to be 0.30 W/g. The microstructure of FD 8 h samples was maintained very well due to the whole cell structure between the center and the edge of the kiwifruit. This is because that the moisture content at the edges and centers was identical in the FD‐MVD samples with FD 8 h. However, in FD‐MVD samples with FD 10 h and FD 12 h, the moisture content at the center was significantly higher than at the outer ring, which resulted in obvious shrinking and folding of cell structure. Therefore, FD 12 h was considered the optimal moisture conversion point because it resulted in a good appearance quality. FD 8 h was the optimal moisture transfer point when the microstructure, drying time and drying energy were considered jointly.Practical ApplicationsKiwifruit is rich in nutrients and is planted in over 80 countries worldwide. FD method used to dry kiwifruit consumes large amounts of energy although results in good quality products. FD‐MVD dried kiwifruit requires less energy to produce and its quality is similar to that of kiwifruit subjected to FD only. This study describes a new, low energy method of kiwifruit drying that has possible implications for large‐scale commercial production. In addition, effect of different cell structure of center and edge in kiwifruit on microstructure afford a new quality judgment method during drying for similar materials.