•Reflectance, Transmittance, Fluorescence, Raman and integrated imaging is covered.•Instrumentation is discussed with special attention for real-time imaging.•The major steps in hyperspectral data ...analysis are discussed.•Overview of applications in external and internal quality evaluation.•Review of applications in food safety detection.
In the past 20 years, hyperspectral imaging has been widely investigated as an emerging, promising technology for evaluating quality and safety of horticultural products. This technology has originated from remote sensing and joins the domains of machine vision and point spectroscopy to provide superior image segmentation for the detection of defects and contaminations, and to map the chemical composition. Thanks to the advancements in instrumentation and data analysis in the past two decades, hyperspectral imaging technology has evolved into a powerful nondestructive inspection tool and the scope of applications in postharvest quality and safety evaluation has expanded tremendously. In this article, different imaging modes (reflectance, transmittance, fluorescence and Raman) and their combinations, and the potential for real-time acquisition of hyperspectral images at industry relevant speeds are first discussed in terms of their advantages and disadvantages. Next reviewed are different data processing/analysis methods and associated steps from data pre-processing over the spectral and spatial domains to the actual model building and performance evaluation. An overview is then given of hyperspectral imaging applications for external quality and defect evaluation, internal quality and maturity assessment, and food safety detection of horticultural products. Finally, a brief discussion is presented on the challenges and opportunities in future development and application of hyperspectral imaging technology in food quality and safety evaluation of horticultural products.
•Hyperspectral imaging was exploited for nondestructively predicting pork attributes.•A more effective data analysis method for resolving “hypercube” was presented.•Gompertz parameters were evaluated ...for predicting different attributes of pork.•One promising tool for monitoring the multiple attributes of pork was provided.
Rapid and nondestructive methods for predicting meat quality and safety attributes are of great concerns at present. A Hyperspectral imaging technique was investigated for evaluating pork meat tenderness and Escherichia coli (E. coli) contamination in this study. Totally 31 samples were used for hyperspectral imaging in the spectral range of 400–1100nm. A novel method by Modified Gompertz function was exploited to extract the scattering characteristics of pork meat from the spatially-resolved hyperspectral images. Gompertz parameters α, β, ε and δ which can represent different optical meanings were derived by curve-fitting to the original scattering profiles. The fitting coefficients were all around 0.99 between 470 and 960nm, which indicating the effective interpretation by Gompertz function. Multi-linear regression models were established using both individual parameters and integrated parameters, and the results showed that Gompertz parameter δ was superior to other individual parameters for both pork meat tenderness and E. coli contamination, and the integrated parameter can perform better than individual parameters. The validation results (RCV) by the integrated parameter method were 0.949 and 0.939 for pork meat tenderness and E. coli contamination respectively. The study demonstrated that hyperspectral imaging technique combined with Gompertz function was potential for rapid determination of pork meat tenderness and E. coli contamination, and so hopefully to provide a promising tool for monitoring the multiple attributes concerning meat quality and safety.
Real-time detection of frozen meat freshness without thawing is important. This study investigates inspection of frozen pork quality attributes without thawing using fluorescence hyperspectral ...imaging (HSI). Partial least squares regression (PLSR) models were developed based on fluorescence spectra for total volatile basic nitrogen (TVB-N), pH, L*, a*, and b*, and compared with PLSR models based on visible/near-infrared (Vis/NIR) HSI of the same samples. Competitive adaptive reweighted sampling was used to select key fluorescence wavelengths related to each indicator. The correlation coefficients of prediction (Rp) of the models established by fluorescence spectra, with optimal pre-treatment for TVB-N, pH, L*, a*, and b*, were 0.9447, 0.9037, 0.6602, 0.8686, and 0.8699, respectively. Except for L*, fluorescence HSI-based model performance was better than that of Vis-NIR HSI. Model performance was further improved using selected key wavelengths. Results demonstrated that fluorescence HSI could determine freshness indicators of frozen pork without thawing.
•Fluorescence HSI was used for the first time to assess frozen pork freshness.•Relationships between fluorescence peaks and freshness indicators were recognized.•PLSR models were compared based on fluorescence HSI and Vis/NIR HSI.•Key wavelengths were selected for each freshness indicators of frozen pork.
Hyperspectral scattering is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models for ...describing the hyperspectral scattering profiles over the spectral region between 450
nm and 1000
nm in order to select an optimal model for predicting fruit firmness and soluble solids content (SSC) of ‘Golden Delicious’ apples. Ten modified Lorentzian distribution functions of various forms were proposed to fit the spectral scattering profiles at individual wavelengths, each of which gave superior fitting to the data with the average correlation coefficient (
r) being greater than 0.995. Mathematical equations were derived to correct the spectral scattering intensity and distance by taking into account the instrument response and individual apples’ size. The 10 modified Lorentzian distribution functions were compared for predicting fruit firmness and SSC using multi-linear regression and cross-validation methods. The modified Lorentzian function with three parameters (representing scattering peak value, width and slope) gave good predictions of fruit firmness with
r
=
0.894 and the standard error of prediction (S.E.P.) of 6.14
N, and of SSC with
r
=
0.883 and S.E.P.
=
0.73%. Twenty-one and 23 wavelengths were needed to obtain the best predictions of fruit firmness and SSC, respectively. This new function, coupled with the scattering profile correction methods, improved the hyperspectral scattering technique for measuring fruit quality.
•An online optical sensing system was developed to detect apple qualities.•ILE-WSM method was proposed to complete the apple image segmentation.•NSR method was proposed to eliminate the scattering ...effects in the raw spectra.•ILE-WSM method was effective with the surface bruises detection accuracy of 97.3 %.•NSR method had better effective compare with other existing preprocessing methods.
An optical sensing system for the detection of surface bruises and the internal qualities of apples has been developed. Isohypse line extraction combined with marker constraint watershed segmentation (ILE-WSM), as a method to resolve the uneven brightness problem in apple images during bruise detection was investigated. The method has three steps: first, morphological filtering to reduce the random noise in the raw images; second, the ILE to locate the bruise position in the de-noised images; and finally, the WSM to complete the final image segmentation. For a 300 undamaged and bruised apples, the correct classification rate was 97.3 % using the ILE-WSM method, showing better segmentation ability than the Otsu method. For internal quality detection, the normalized spectral ratio (NSR) method has been proposed to correct the light scattering effects in the raw spectra. The NSR has the advantages of a simple calculation and high precision over the other methods. The final detection models for the apple soluble solids content (SSC) and dry matter content (DMC) were built on the key variables after selection by the competitive adaptive reweighted sampling (CARS) method. The root mean square error of the prediction dataset (RMSEP) and the correlation coefficient of the prediction dataset (Rp) of the final model prediction for the SSC and DMC were 0.412 % and 0.957 and 0.602 % and 0.937, respectively. The size of the whole system was 1600 mm × 500 mm × 1500 mm and the total time required to inspect each apple was 0.42 s. The optical sensing system can successfully be applied to apple surface bruise and internal quality detection.
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•NIR models for soluble solids content of apple were developed.•Both short wave (SWNIR) and long wave (LWNIR) wavelength ranges were considered.•Color compensation significantly ...improves prediction accuracy for SWNIR.•Nonlinear calibration models were better than linear ones.•Wavelength selection and latent variable construction algorithms were investigated.
Shortwave near infrared (SWNIR) and long wave near infrared (LWNIR) spectroscopy with a novel color compensation method were compared to predict soluble solids content of apple. Linear and nonlinear regression models were considered. Eventually, independent component analysis-support vector machine (ICA-SVM) models proved to be superior to other nonlinear models. Rp was 0.9398 and RMSEP was 0.3870% for the optimal model of SWNIR, while Rp was 0.9455 and RMSEP was 0.3691% for that of LWNIR. Moreover, the results showed that color compensation could significantly improve the prediction performance of SWNIR model. Our work implies that SWNIR with color compensation has an obvious prospect in practical industrial use for real-time monitoring apple quality.
A rapid nondestructive method based on hyperspectral scattering technique for simultaneous determination of pork tenderness and Escherichia coli (E. coli) contamination was studied in the research. ...The hyperspectral scattering images of thirty-one pork samples were collected in 400–1100nm, and the scattering profiles were then fitted by Lorentzian distribution function to give three parameters a (asymptotic value), b (peak value) and c (full width at b/2). The combined parameters of (b-a), (b-a)×c, (b-a)/c and “a&b&c” were used to develop multi-linear regression (MLR) models for prediction of pork tenderness and E. coli contamination. It was shown that MLR models developed using parameters a, b, (b-a) and (b-a)/c can give high correlation coefficients of 0.831, 0.860, 0.856 and 0.930 respectively for pork tenderness prediction. For E. coli contamination of pork, MLR models based on parameters a and “a&b&c” can give high RCV of 0.877 and 0.841 respectively.
Multispectral scattering is a promising technique for non-destructive sensing of multiple quality attributes of apple fruit. This research developed new, improved methods for processing and analyzing ...multispectral scattering profiles in order to design and build a better multispectral imaging system for real-time measurement of apple fruit firmness and soluble solids content. Spectral scattering images were obtained from Golden Delicious apples at four selected wavebands (680, 800, 900 and 950
nm) using a common-aperture multispectral imaging system. The scattering intensity and distance were corrected by incorporating the effect of individual apples’ size. A new method of correcting scattering image profiles was proposed to minimize the effect of light source variation on the calculation of scattering function parameters. Modified Gompertz and Lorentzian functions with four parameters and their variants were evaluated and compared for predicting fruit firmness and soluble solids content using multi-linear regression and cross-validation methods. The modified Gompertz function had better prediction results with a correlation coefficient (
r) of 0.896 and a standard error of prediction (SEP) of 6.50
N for firmness, and
r
=
0.816 and SEP
=
0.92% for soluble solids content. This new function, coupled with the scattering profile correction methods, improved the multispectral scattering technique for measuring fruit quality.
Current methods for detecting the bacterial contamination of meat are time-consuming, labor-intensive, and giving retrospective information; therefore, the objective of this study was to investigate ...the feasibility of hyperspectral scattering imaging for rapid and nondestructive determination of total viable count (TVC) in pork meat. Fresh pork meat was purchased from a local market and stored at 10 °C for 1–15 days. In total, 59 samples were used in this study, and three to four samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological analysis on each day of the experiment. Both the Lorentzian function and the Gompertz function were exploited to interpret the scattering profiles of pork meat samples, and good fitting results were obtained between 472 and 1,000 nm. Stepwise multiple linear regression (SMLR) method was performed to establish the prediction models, and moving average method with the filter size ranging from 3-point to 15-point was applied to improve the modeling results, respectively. Among the models established, the models developed by the Lorentzian parameter
b
and the Gompertz parameter
β
performed best for predicting pork meat TVC, with the correlation coefficient of validation set (Rv) of 0.94 and 0.93, respectively, after 13-point and 11-point moving average. The Lorentzian parameter
a
and the Gompertz parameters
α
and
δ
can also give good prediction results, with Rv of 0.83, 0.88, and 0.82, respectively. The results demonstrated that hyperspectral scattering imaging combined with the Lorentzian function and the Gompertz function can be a powerful tool for evaluating the microbial safety of meat in the future.
Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often ...time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface–enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat.
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