Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging

Two different near infrared spectrometric systems were used to determine soluble solids content (SSC) of intact apple, including a portable near infrared (NIR) spectrometer and an online NIR system. The pretreatment methods were applied to improve the predictive results. The moving average smoothing was significant. The effective wavelength regions were chosen by interval partial least squares (iPLS) and backward iPLS (Bipls). Then the models were developed by partial least square regression (PLSR) and least square support machine (LS-SVM). Performance comparisons were made in the context of 30 unknown samples prediction. The LS-SVM models were better than others with correlation coefficient (R) and root mean square error of prediction (RMSEP) of (0.88, 0.80oBrix) and (0.82, 1.01oBrix) for portable and online measurement mode, respectively. The results demonstrated that the online measurement mode was not as well as the portable.

Currently, destructive methods are often used to measure the quality parameters of agricultural products. These methods are often complex, time consuming and costly. Recently, studying to find a solution to the disadvantages of destructive methods has become a major challenge for researchers. Non-destructive methods can be useful for the rapid detection of the quality parameters of agricultural products. In this study, hyperspectral imaging was used to evaluate the non-destructive quality parameters of Red Delicious (Red Delicious) and Golden Delicious (Golden Delicious) apples, including pH, soluble solids content (SSC), titratable acid (TA) and total phenol (TP). In order to predict the quality characteristics of apples, the partial least squares (PLS) method with different pre-processing was used. The developed models were evaluated using the root mean square parameters of RMSECV validation error, correlation coefficient (Rcv) and standard deviation ratio (SDR). The results showed that in Red Delicious, for pH, TA, SSC and TP the best forecasting methods were SNV, SNV, MSC and normalized pre-processing with the regression coefficient values of 0.9919, 0.9939, 0.9909 and 0.9899, respectively. In Golden Delicious (Golden Delicious), for pH, TA, SSC and TP, the first derivative, (smoothing and second derivative), normalize (and SNV and normalize) preprocessors were selected as the best prediction models, with values of 0.9989, 0.9989, 0.9999 and 0.9989, respectively. The results related to an artificial neural network also showed that in hyperspectral imaging, the best state of the feed-forward network structure with the LM training algorithm was R = 0.93, Performance = 0.005 and RMSE = 0.03 in 325 inputs, 5 outputs and 2 hidden layers. The results showed that hyperspectral imaging has different predictive capabilities for the qualitative characteristics studied in this study with high accuracy.

Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the , and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the , , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

Detection of mango soluble solid content using hyperspectral imaging technology

For rapid detection of soluble solid content (SSC) in beer, visible/near infrared (Vis/NIR) spectra of 360 beer samples were collected by using Vis/NIR spectroradiometer. Principal component analysis (PCA) was applied for reducing the dimensionality in order to decrease the overlapped information of raw spectral data, 6 principal components (PCs) were selected. The samples were randomly separated into calibration set and validation set, and least squares support vector machine (LS-SVM) algorithm was used to build calibration model of SSC in beer, then the model was employed for the prediction of the validation set. Correlation coefficient (r) of prediction and root mean square error prediction (RMSEP) were used as evaluation standards, and the results indicated that r and RMSEP for the prediction of SSC were 0.9829 and 0.1506. The precision of prediction was obviously higher than that of back-propagation artificial neural network (BP-ANN) and partial least squares (PLS) models, hence PCA and LS-SVM algorithm model with high prediction precision could be applied to the determination of SSC in beer.

Blueberry is becoming increasingly popular because consumers like its flavor and antioxidant capacity for anti-aging. Firmness and soluble solids content (SSC) are the most important internal quality attributes for blueberries, and they are directly related to the maturity/ripeness and shelf life. The current blueberry firmness inspection systems are based on the mechanical vibration or impact principle, which could induce tissue damage, while computer vision systems only inspect external quality (i.e., size and color). This study reports on the prediction of firmness and SSC, using hyperspectral reflectance imaging in the region of 500-1,000 nm. A line scan system was used to acquire hyperspectral images of 300 blueberries for two fruit orientations (i.e., stem and calyx ends). Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on firmness and SSC prediction. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of hyperspectral imaging technique for sorting and grading blueberries to enhance their quality and marketability.

Non-destructive and in-site estimation of apple quality and maturity by hyperspectral imaging

Quantification of n-3 polyunsaturated fatty acids (PUFA) is an important aspect in quality control of designer eggs. Prediction of these fatty acids would be of value for egg grading stations. The aim of the present study was to evaluate the ability of hyperspectral imaging (HSI) to classify the eggs according their content in n-3 PUFA as a rapid and non-destructive technique. Furthermore, HSI was used to predict the content of n-3 PUFA in designer eggs. Normal eggs, eggs containing 75 mg and eggs containing 125 mg docosahexaenoic acid were purchased from a grocery store. After hyperspectral transmittance images were collected in the spectral region of 900–1,700 nm, the eggs were broken and analyzed chemically in order to measure fatty acid content. Regions of interest (ROIs) of about 3,000 pixels from each egg were selected. K-means analysis was performed to classify eggs into the three aforementioned types. The classification accuracy was 100%. A partial least-squares regression model was built and used to link the ROIs with results obtained from the chemical analysis. The correlation coefficients between the measured and predicted values of alpha-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid were 0.94, 0.73 and 0.87, respectively. The root mean square error values for the validation sets were 38.65, 3.31 and 7.03, respectively. These results showed that HSI could be used both for discriminating the eggs between normal and designer eggs and for prediction of n-3 PUFA content in eggs.

Ripeness evaluation of kiwifruit by hyperspectral imaging

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 imaging technology was used to assess the soluble solids content (SSC), titrable acidity content (TAC), and firmness of hami melons. The mean spectra were extracted from the regions of interest (ROI) of the hyperspectral images of each hami melon. Spectral data were first pretreated with different preprocessing methods and analyzed using the partial least squares (PLS) method to build calibration models. However, full spectral data contain a great number of redundant and colinear variables that lead to poor predicting ability of calibration models. Three typical variable selection methods, i.e., the genetic algorithm (GA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used to extract effective wavelength variables from the full spectra for determination of SSC, TAC, and firmness of hami melon. The results showed that the SNV‐CARS‐PLS model based on 51 and 40 wavelength variables achieved the optimal performance for SSC and TAC compared with full spectral SNV‐PLS, SNV‐GA‐PLS, and SNV‐SPA‐PLS models. The RP, RMSEP, and RPD of SNV‐CARS‐PLS model for SSC were 0.9606, 0.3816, and 3.598, respectively, and 0.9125, 0.0263, and 2.445 for TAC, respectively. The best model for predicting firmness was RAW‐CARS‐PLS model based on 57 wavelength variables with the RP RMSEP, and RDP values of 0.8671, 20.05, and 1.996, respectively. Overall, the results indicate that the CARS is a powerful method for selecting effective wavelength variables and demonstrated the feasibility of using hyperspectral imaging technology as a fast and nondestructive method for simultaneous detection of SSC, TAC, and firmness of hami melons.Practical applicationsThe quality of melon has a direct relationship with the soluble solids content, titrable acidity content, and texture of melon. To ensure the best taste and highest nutritional value, it is better for melons to be harvested when completely mature. Nowadays, melons were harvested according to different fruit development times after flowering. Traditional methods for detection of internal qualities and maturity of melon mainly focus on the appearance of melon and depend on the feelings of professionals, which are tedious, time‐consuming, expensive, and greatly influenced by subjective factors. More than anything, Traditional methods are hard to be highly accurate. Hyperspectral imaging technology has the advantages of rapid and nondestructive. The results showed that hyperspectral imaging technology for the simultaneous detection of soluble solids content, titrable acidity content, and firmness of melon is feasible. And the results can provide a reference for detecting large fruits by using hyperspectral imaging technology

<abstract> <bold>Abstract.</bold> Soluble solid is an important quality attribute of vegetables and fruits. Development of nondestructive technology for soluble solid measurement will help the industry provide better vegetables and fruits for consumers. A prototype of hyperspectral transmittance imaging system with two 50W halogen lamps and one height-limiting board was developed. Spectral transmittance images of tomatoes were acquired from the loophole of the height limiting board before background segmentation using special bands. The spectral data were analyzed using the partial least squares (PLS) analysis. PLS model was established to predict the soluble solid content (SSC) of tomatoes using average spectral information of regions of interest. The correlation coefficients (r) with the spectral range (720-990 nm) for predicting SSC was 0.83 with RMSEC of 0.175 and RMSEP of 0.357 respectively. The results of this work show that the hyperspectral diffuse transmittance imaging technique is promising for assessing soluble solid content of tomatoes.

A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R 2 cv) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process.

Hyperspectral imaging for the determination of potato slice moisture content and chromaticity during the convective hot air drying process