A new method for detection both external and internal quality attributes of tomato was proposed in this paper. The comprehensive quality detection could be completed by the image and spectra analyses based on the optical sensing system. The images processing method contained three steps: (1) morphological filtering; (2) binarization and (3) circle fitting. The first step was applied to reduce the random noises in the raw images. The second step was aimed to obtain the binary images that contained the contour information of the tomato edge. And the circle fitting algorithm was used to obtain the final diameter information of the tomato samples. The values of R and the RMSE for size prediction results of the tomato samples were 0.9813 and 1.269 mm, respectively. For the spectra analysis, the light scatter effects, including addition coefficient and multiplication coefficient in the raw spectra, were the main reasons for the calibration failure of the multivariate linear model such as PLS, MLR and PCR. Thus, the NSR method was used to eliminate the light scatter effects in this paper. Compared with the other method, NSR method was advantages in higher prediction performance and the simpler calculation. The RMSEP values of the final PLS model were 0.2936 % and 2.0129 a.u. for the SSC and a*, respectively. Thus, the optical sensing system combined with effective information processing method was able to detect the tomato external and internal quality attributes, which could be more suitable to apply in the food processing enterprise in the practice.
Gradient Temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry (DSC) to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements that occur at or near phase transitions. 20 Mb threedimensional data arrays with 1.0 or 0.2°C increments allow complete assignment of solid, liquid and transition state vibrational modes, including low intensity/frequency vibrations that cannot be readily analyzed with conventional Raman. We compared GTRS and DSC data for commercial fish oil supplements that are excellent sources of docosahexaenoic acid (DHA; 22:6n-3) and eicosapentaenoic acid (EPA; 20:5n-3). Krill oils and whole fish and shellfish have nearly all their PUFA contained within phospholipids (PL). Development of any fast-throughput optical identification system for seafood products will require improved PL Raman data. We also analyzed molecularly hydrated PL with DHA and other unsaturated lipids. Each fish oil and PL have a unique, distinctive response to the thermal gradient, which graphically and spectroscopically differentiates them. The entire set or any subset of the any of the contour plots, first derivatives or second derivatives can be utilized to create a graphical standard to quickly authenticate a given product or source material.
Commercial Raman systems generally conduct imaging and spectroscopy measurements at subcentimeter scales. Such small spatial ranges cannot be used to inspect food samples with large surface areas (e.g., tomato fruit and beef steak), which is not convenient for food experiments. A line-scan macro-scale Raman system has been developed using a 785 nm line laser to implement high-throughput Raman chemical imaging (RCI) for food safety and quality research. A one-axis positioning table is used to move the samples to accumulate hyperspectral data using a pushbroom method. A dispersive Raman spectrograph is used in the system, which can be configured to backscattering RCI mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In-house developed LabVIEW software is used to fulfill functions for system control, hardware parameterization, and data transfer. The systems is flexible and versatile for food test, and it has been used to evaluate safety and quality of various food and agricultural products, such as detecting chemical adulterants mixed in food powders, mapping carotenoid content on carrot cross section, imaging whole surface of pork shoulder, and authenticating foods and ingredients through packages.
In recent years, the quality and safety issues related to flour and pasta products have attracted great attention from the society. The quality of flour will directly affect the quality of downstream pasta products, as well as the physical and mental health and economic benefits of consumers. In this study, the illegal additive benzoyl peroxide in flour was the research object, and the rapid real-time non-destructive detection of benzoyl peroxide in flour was realized by Raman hyperspectral technique. By comparing the Raman spectra of pure benzoyl peroxide and pure flour, several Raman spectral characteristic peaks of benzoyl peroxide and their assignments were found. Characteristic peaks with strong signal at 1001 cm-1 and 1777 cm-1 were extracted for quantitative analysis. A gradient concentration of benzoyl peroxide-doped flour samples from 1% to 0.05% was prepared. And a series of pretreatment including S-G 5-point smoothing and background removal were performed to extract the number of effective benzoyl peroxide pixels in the mixed sample. And the proportion of benzoyl peroxide pixel points in total pixel points with different benzoyl peroxide concentrations was acquired. By comparing the relationship between the proportion and the concentration of benzoyl peroxide, a quantitative analysis model for the benzoyl peroxide doping in flour was established. The verification results show that there was good correlation between the proportion and the concentration of benzoyl peroxide. Both the averaged benzoyl peroxide signal intensities of effective pixel points and the number of effective pixels were combined for quantitative analysis. The research provided a methodological support for the detection of additives in flour by hyperspectral techniques and was a reference for the detection of dopants in food.
Turmeric powder (Curcuma longa L.) is known for its use in foods, in medicine, and as a cosmetic. In recent years, economically driven contamination of turmeric powder with different chemicals is increasing. This study used a 1064 nm hyperspectral Raman imaging system for detection of Sudan Red G dye contamination in turmeric powder. Sudan Red was mixed with turmeric powder at five concentration levels (1%, 5%, 10%, 15%, and 20%- w/w). Each mixture sample was packed in a sample container. A Raman chemical image of each sample was acquired across the 7.5 mm x 7.5 mm surface area using a 0.25 mm step size. The spectral fingerprint of turmeric and Sudan Red were identified and used to obtain a binary image from the Raman chemical image of each sample. A simple threshold method was applied to convert the contaminant pixels into white pixels and turmeric pixels into the black (background) pixels. The detected Sudan Red pixels were correlated with the actual concentration in the sample. The result shows that the Sudan Red pixels in the sample image is linearly correlated (R2 = 0.99) with the actual concentration of the sample. This study demonstrated the 1064 nm hyperspectral Raman imaging system as a potential tool to detect chemical contaminants in turmeric powder.
Spice powders are used as food additives for flavor and color. Economically motivated adulteration of spice powders by color dyes is hazardous to human health. This study explored the potential of a 1064 nm Raman chemical imaging system for identification of azo color contamination in spice powders. Metanil yellow and Sudan-I, both azo compounds, were mixed separately with store-bought turmeric and curry powder at the concentration ranging from 1 % to 10 % (w/w). Each mixture sample was packed in a shallow nickel-plated sample container (25 mm x 25 mm x 1 mm). One Raman chemical image of each sample was acquired across the 25 mm x 25 mm surface area using a 0.25 mm step size. A threshold value was applied to the spectral images of metanil yellow mixtures (at 1147 cm-1) and Sudan-I mixtures (at 1593 cm-1) to obtain binary detection images by converting adulterant pixels into white pixels and spice powder pixels into the black (background) pixels. The detected number of pixels of each contaminant is linearly correlated with sample’s concentration (R2 = 0.99). This study demonstrates the 1064 nm Raman chemical imaging system as a potential tool for food safety and quality evaluation.
Economically motivated adulteration and fraud to food powders are emerging food safety risks that threaten the health of the general public. In this study, targeted and non-targeted methods were developed to detect adulterants based on macro-scale Raman chemical imaging technique. Detection of potassium bromate (PB) (a flour improver banned in many countries) mixed in wheat flour was used as a case study to demonstrate the developed methods. A line-scan Raman imaging system with a 785 nm line laser was used to acquire hyperspectral image from the flour-PB mixture. Raman data analysis algorithms were developed to fulfill targeted and non-targeted contaminant detection. The targeted detection was performed using a single-band Raman image method. An image classification algorithm was developed based on single-band image at a Raman peak uniquely selected for the PB. On the other hand, a mixture analysis and spectral matching method was used for the non-targeted detection. The adulterant was identified by comparing resolved spectrum with reference spectra stored in a pre-established Raman library of the flour adulterants. For both methods, chemical images were created to show the PB particles mixed in the flour powder.
Proper chemical analyses of materials in sealed containers are important for quality control purpose. Although it is feasible to detect chemicals at top surface layer, it is relatively challenging to detect objects beneath obscuring surface. This study used spatially offset Raman spectroscopy (SORS) method to detect urea, ibuprofen and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785 nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. With increasing offset distance, the fraction of information from the deeper subsurface material increased compared to that from the top surface material. The series of measurements was analyzed to differentiate and identify the top surface and subsurface materials. Containing mixed contributions from the powder and capsule, the SORS of each sample was decomposed using self modeling mixture analysis (SMA) to obtain pure component spectra of each component and corresponding components were identified using spectral information divergence values. Results show that SORS technique together with SMA method has a potential for non-invasive detection of chemicals at deep subsurface layer.
Turmeric is well known for its medicinal value and is often used in Asian cuisine. Economically motivated contamination of turmeric by chemicals such as metanil yellow has been repeatedly reported. Although traditional technologies can detect such contaminants in food, high operational costs and operational complexities have limited their use to the laboratory. This study used Fourier Transform Raman Spectroscopy (FT-Raman) and Fourier Transform - Infrared Spectroscopy (FT-IR) to identify metanil yellow contamination in turmeric powder. Mixtures of metanil yellow in turmeric were prepared at concentrations of 30%, 25%, 20%, 15%, 10%, 5%, 1% and 0.01% (w/w). The FT-Raman and FT-IR spectral signal of pure turmeric powder, pure metanil yellow powder and the 8 sample mixtures were obtained and analyzed independently to identify metanil yellow contamination in turmeric. The results show that FT-Raman spectroscopy and FT-IR spectroscopy can detect metanil yellow mixed with turmeric at concentrations as low as 1% and 5%, respectively, and may be useful for non-destructive detection of adulterated turmeric powder.
Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.
Raman spectroscopy is a useful, rapid, and non-destructive method for both qualitative and quantitative evaluation of chemical composition. However it is important to measure the depth of penetration of the laser light to ensure that chemical particles at the very bottom of a sample volume is detected by Raman system. The aim of this study was to investigate the penetration depth of a 785nm laser (maximum power output 400mw) into three different food powders, namely dry milk powder, corn starch, and wheat flour. The food powders were layered in 5 depths between 1 and 5 mm overtop a Petri dish packed with melamine. Melamine was used as the subsurface reference material for measurement because melamine exhibits known and identifiable Raman spectral peaks. Analysis of the sample spectra for characteristics of melamine and characteristics of milk, starch and flour allowed determination of the effective penetration depth of the laser light in the samples. Three laser intensities (100, 200 and 300mw) were used to study the effect of laser intensity to depth of penetration. It was observed that 785nm laser source was able to easily penetrate through every point in all three food samples types at 1mm depth. However, the number of points that the laser could penetrate decreased with increasing depth of the food powder. ANOVA test was carried out to study the significant effect of laser intensity to depth of penetration. It was observed that laser intensity significantly influences the depth of penetration. The outcome of this study will be used in our next phase of study to detect different chemical contaminants in food powders and develop quantitative analysis models for detection of chemical contaminants.
Addition of edible and inedible chemical contaminants in food powders for purposes of economic benefit has become a recurring trend. In recent years, severe health issues have been reported due to consumption of food powders contaminated with chemical substances. This study examines the effect of spatial resolution used during spectral collection to select the optimal spatial resolution for detecting melamine in milk powder. Sample depth of 2mm, laser intensity of 200mw, and exposure time of 0.1s were previously determined as optimal experimental parameters for Raman imaging. Spatial resolution of 0.25mm was determined as the optimal resolution for acquiring spectral signal of melamine particles from a milk-melamine mixture sample. Using the optimal resolution of 0.25mm, sample depth of 2mm and laser intensity of 200mw obtained from previous study, spectral signal from 5 different concentration of milk-melamine mixture (1%, 0.5%, 0.1%, 0.05%, and 0.025%) were acquired to study the relationship between number of detected melamine pixels and corresponding sample concentration. The result shows that melamine concentration has a linear relation with detected number of melamine pixels with correlation coefficient of 0.99. It can be concluded that the quantitative analysis of powder mixture is dependent on many factors including physical characteristics of mixture, experimental parameters, and sample depth. The results obtained in this study are promising. We plan to apply the result obtained from this study to develop quantitative detection model for rapid screening of melamine in milk powder. This methodology can also be used for detection of other chemical contaminants in milk powders.
Benzoyl peroxide is a common flour additive that improves the whiteness of flour and the storage properties of flour products. However, benzoyl peroxide adversely affects the nutritional content of flour, and excess consumption causes nausea, dizziness, other poisoning, and serious liver damage. This study was focus on detection of the benzoyl peroxide added in wheat flour. A Raman scattering spectroscopy system was used to acquire spectral signal from sample data and identify benzoyl peroxide based on Raman spectral peak position. The optical devices consisted of Raman spectrometer and CCD camera, 785 nm laser module, optical fiber, prober, and a translation stage to develop a real-time, nondestructive detection system. Pure flour, pure benzoyl peroxide and different concentrations of benzoyl peroxide mixed with flour were prepared as three sets samples to measure the Raman spectrum. These samples were placed in the same type of petri dish to maintain a fixed distance between the Raman CCD and petri dish during spectral collection. The mixed samples were worked by pretreatment of homogenization and collected multiple sets of data of each mixture. The exposure time of this experiment was set at 0.5s. The Savitzky Golay (S-G) algorithm and polynomial curve-fitting method was applied to remove the fluorescence background from the Raman spectrum. The Raman spectral peaks at 619 cm-1, 848 cm-1, 890 cm-1, 1001 cm-1, 1234 cm-1, 1603cm-1, 1777cm-1 were identified as the Raman fingerprint of benzoyl peroxide. Based on the relationship between the Raman intensity of the most prominent peak at around 1001 cm-1 and log values of benzoyl peroxide concentrations, the chemical concentration prediction model was developed. This research demonstrated that Raman detection system could effectively and rapidly identify benzoyl peroxide adulteration in wheat flour. The experimental result is promising and the system with further modification can be applicable for more products in near future.
Residual pesticides in fruits and vegetables have become one of the major food safety concerns around the world. At present, routine analytical methods used for the determination of pesticide residue on the surface of fruits and vegetables are destructive, complex, time-consuming, high cost and not environmentally friendly. In this study, a novel Surface Enhanced Raman Spectroscopy (SERS) method with silver colloid was developed for fast and sensitive nondestructive detection of residual pesticides in fruits and vegetables by using a self-developed Raman system. SERS technology is a combination of Raman spectroscopy and nanotechnology. SERS can greatly enhance the Raman signal intensity, achieve single-molecule detection, and has a simple sample pre-treatment characteristic of high sensitivity and no damage; in recent years it has begun to be used in food safety testing research. In this study a rapid and sensitive method was developed to identify and analyze mixed pesticides of chlorpyrifos, deltamethrin and acetamiprid in apple samples by SERS. Silver colloid was used for SERS measurement by hydroxylamine hydrochloride reduced. The advantages of this method are seen in its fast preparation at room temperature, good reproducibility and immediate applicability. Raman spectrum is highly interfered by noise signals and fluorescence background, which make it too complex to get good result. In this study the noise signals and fluorescence background were removed by Savitzky-Golay filter and min-max signal adaptive zooming method. Under optimal conditions, pesticide residues in apple samples can be detected by SERS at 0.005 μg/cm2 and 0.002 μg/cm2 for individual acetamiprid and thiram, respectively. When mixing the two pesticides at low concentrations, their characteristic peaks can still be identified from the SERS spectrum of the mixture. Based on the synthesized material and its application in SERS operation, the method represents an ultrasensitive SERS performance in apple samples detection without sample pre-treatment, which indicates that it could be served as a useful means in monitoring pesticide residues.
Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned
stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork.
Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the
technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study
used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into
three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH24. Total of 66 samples were used in the
experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in
laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter
correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model
was developed to classify the samples based on their comprehensive qualities. The results showed that the classification
model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system
developed in this study being simple and easy to use, results being promising, the system can be used in meat processing
industry for real time, non-destructive and rapid detection of pork qualities in future.
Different chemicals are sprayed in fruits and vegetables before and after harvest for better yield and longer shelf-life of
crops. Cases of pesticide poisoning to human health are regularly reported due to excessive application of such
chemicals for greater economic benefit. Different analytical technologies exist to detect trace amount of pesticides in
fruits and vegetables, but are expensive, sample destructive, and require longer processing time. This study explores the
application of Raman spectroscopy for rapid and non-destructive detection of pesticide residue in agricultural products.
Raman spectroscopy with laser module of 785 nm was used to collect Raman spectral information from the surface of
Gala apples contaminated with different concentrations of commercially available organophosphorous (48%
chlorpyrifos) pesticide. Apples within 15 days of harvest from same orchard were used in this study. The Raman spectral
signal was processed by Savitzky-Golay (SG) filter for noise removal, Multiplicative Scatter Correction (MSC) for drift
removal and finally polynomial fitting was used to eliminate the fluorescence background. The Raman spectral peak at
677 cm-1 was recognized as Raman fingerprint of chlorpyrifos. Presence of Raman peak at 677 cm-1 after fluorescence
background removal was used to develop classification model (presence and absence of pesticide). The peak intensity
was correlated with actual pesticide concentration obtained using Gas Chromatography and MLR prediction model was
developed with correlation coefficient of calibration and validation of 0.86 and 0.81 respectively. Result shows that
Raman spectroscopy is a promising tool for rapid, real-time and non-destructive detection of pesticide residue in agro-products.
The objectives of this research were to develop a rapid non-destructive method to evaluate the edible quality of chilled pork. A total of 42 samples were packed in seal plastic bags and stored at 4°C for 1 to 21 days. Reflectance spectra were collected from visible/near-infrared spectroscopy system in the range of 400nm to 1100nm. Microbiological, physicochemical and organoleptic characteristics such as the total viable counts (TVC), total volatile basic-nitrogen (TVB-N), pH value and color parameters L* were determined to appraise pork edible quality. Savitzky-Golay (SG) based on five and eleven smoothing points, Multiple Scattering Correlation (MSC) and first derivative pre-processing methods were employed to eliminate the spectra noise. The support vector machines (SVM) and partial least square regression (PLSR) were applied to establish prediction models using the de-noised spectra. A linear correlation was developed between the VIS/NIR spectroscopy and parameters such as TVC, TVB-N, pH and color parameter L* indexes, which could gain prediction results with Rv of 0.931, 0.844, 0.805 and 0.852, respectively. The results demonstrated that VIS/NIR spectroscopy technique combined with SVM possesses a powerful assessment capability. It can provide a potential tool for detecting pork edible quality rapidly and non-destructively.
Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer’s hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.
Apple is the world largest produced and consumed fruit item. At the same time, apple ranks number one among the fruit item contaminated with pesticide. This research focuses on development of laboratory based self-developed software and hardware for detection of commercially available organophosphorous pesticide (chlorpyrifos) in apple surface. A laser light source of 785nm was used to excite the sample, and Raman spectroscopy assembled with CCD camera was used for optical data acquisition. A hardware system was designed and fabricated to clamp and rotate apple sample of varying size maintaining constant working distance between optical probe and sample surface. Graphical Users Interface (GUI) based on LabView platform was developed to control the hardware system. The GUI was used to control the Raman system including CCD temperature, exposure time, track height and track centre, data acquisition, data processing and result prediction. Different concentrations of commercially available 48% chlorpyrifos pesticide solutions were prepared and gently placed in apple surface and dried. Raman spectral data at different points from same apple along the equatorial region were then acquired. The results show that prominent peaks at 341cm-1, 632cm-1 and 680 cm-1 represent the pesticide residue. The laboratory based experiment was able to detect pesticide solution of 20ppm within 3 seconds. A linear relation between Raman intensity and pesticide residue was developed with accuracy of 97.8%. The result of the research is promising and thus is a milestone for developing industrially desired real time, non-invasive pesticide residue detection technology in future.
The potential of Raman spectroscopy in the analysis of low concentration organic contaminants on apples' surface was
evidenced in this study. Chlorpyrifos, an organophosphorus pesticide, was used as a probe for this purpose. The
characteristic peaks of fingerprints of pesticide on an aluminum substrate and apple fruit cuticle without pesticide
residue were acquired first. Then a concentration range of chlorpyrifos (commercial products at 40%) solutions were
made using deionised and distilled water. Single 100 μL droplets of the chlorpyrifos solutions were placed gently on
apple fruit cuticles and left to dry before analysis. Through comparative analysis of the Raman spectra data collected,
341, 632 and 1237cm-1 were identified to detect the chlorpyrifos pesticide residue on apple surface. Based on the
relationship between the Raman intensity of the most prominent peak at around 632cm-1 and the pesticide
concentrations, the limit of detection of ordinary Raman spectrum for chlorpyrifos was estimated to be 48ppm.
Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific
objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality
attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were
packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from
the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature
spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were
fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted
from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to
predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than
combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard
error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a
combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork
freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive
detection of pork freshness.
A rapid nondestructive measurement method for determining the total viable count of chilled pork was studied. Chilled
pork samples were purchased from supermarket and then stored in refrigerator at 4°C. Every 24 hours, hyperspectral
images were collected from the chilled pork samples in 400-1100nm region, in parallel total viable counts were obtained
by classical microbiological plating methods. The 3-parameter modified lorentzian distribution function was applied to
fit the scattering profiles of all samples and the fitting results were satisfactorily high in region 470-943 nm. Then the parameters extracted were used to establish PLSR models. The prediction results for the parameter a, b, c, b×c are 0.945, 0.918, 0.919, 0.935 respectively. The study show that the hyperspectral technology can accurately tracks the increase of total viable count of chilled pork during 2-14 days storage at 4°C, and so indicate it a valid tool for assessing the quality and safety properties of chilled pork rapidly and nondestructively in the future.
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