A sequential method for estimating the optical properties of two-layer media with spatially-resolved diffuse reflectance was proposed and validated using Monte Carlo-generated reflectance profiles. The relationship between the penetration depth of detected photons and source-detector separation was first studied. Photons detected at larger source-detector separations generally penetrated deeper into the medium than those detected at small source-detector separations. The effect of each parameter (i.e., the absorption and reduced scattering coefficients (μa and μs′) of each layer, and the thickness of top layer) on reflectance was investigated. It was found that the relationship between the optical properties and thickness of top layer was a critical factor in determining whether photons would have sufficient interactions with the top layer and also penetrate into the bottom layer. The constraints for the proposed sequential estimation method were quantitatively determined by the curve fitting procedure coupled with error contour map analyses. Results showed that the optical properties of top layer could be determined within 10% error using the semi-infinite diffusion model for reflectance profiles with properly selected start and end points, when the thickness of top layer was larger than two times its mean free path (mfp’). And the optical properties of the bottom layer could be estimated within 10% error by the two-layer diffusion model, when the thickness of top layer was less than 16 times its mfp’. The proposed sequential estimation method is promising for improving the estimation of the optical properties of two-layer tissues from the same spatially-resolved reflectance.
A prototype of on-line system developed by ourselves was used to non-destructively inspect orange quality. This system
includes three main parts: machine vision part for fruit external quality detection, visible and near infrared (Vis-NIR)
spectroscopy part for fruit internal quality detection, and weighing part for fruit weight detection. Fruit scrolled on the
roller in the machine vision part, while stopped scrolling before entering the Vis-NIR spectroscopy part. Therefore, fruit
positions and directions were inconsistent for spectra acquisition. This paper was aimed to study the influence of fruit
detection orientation on spectra variation and model estimation performance using the on-line system. The system was
configured to operate at typical grader speeds (0.27m/s or approximately three fruit per second) and detect the light
transmitted through oranges. Stepwise multi linear regression models were developed for fruit with consistent directions
and inconsistent directions in the wavelength range of 600-950 nm, and gave reasonable calibration correlations
R2=0.89-0.92 and low cross validation errors (RMSECV=0.44-0.56%). The calibration model with spherical samples
only turned out the best prediction results, which has lowest RMSEP of 0.56%-0.63% for different fruit orientations. It
can be seen from the study that fruit shape would influence the fruit orientation for spectra aqcuiring of spherical
samples after scrolling, and would further influence the modeling resutls. It is better to acquire spectra and establish
models for sampels with different shapes separately and then applying them based on shape detection resutls to improve
the soluble solid content (SSC) prediction accuracy.
Authenticity is an important food quality criterion. Rapid methods for confirming authenticity or detecting adulteration
are increasingly demanded by food processors and consumers. Near infrared (NIR) spectroscopy has been used to detect
economic adulteration in pork . Pork samples were adulterated with liver and chicken in 10% increments. Prediction and
quantitative analysis were done using raw data and pretreatment spectra. The optimal prediction result was achieved by
partial least aquares(PLS) regression with standard normal variate(SNV) pretreatment for pork adulterated with liver
samples, and the correlation coefficient(R value), the root mean square error of calibration(RMSEC) and the root mean
square error of prediction (RMSEP) were 0.97706, 0.0673 and 0.0732, respectively. The best model for pork meat
adulterated with chicken samples was obtained by PLS with the raw spectra, and the correlation coefficient(R value),
RMSEP and RMSEC were 0.98614, 0.0525, and 0.122, respectively. The result shows that NIR technology can be
successfully used to detect adulteration in pork meat adulterated with liver and chicken.
When vibrational spectra are measured on- or in-line for process analytical or control purposes, the
spectra may fluctuate in response due to fluctuations in environmental conditions, such as temperature
or humidity that must be taken into consideration when developing calibration models. In this paper,
the influence of temperature fluctuations on visible and near-infrared (Vis/NIR) spectra and their effect
on the predictive power of calibration models, partial least squares (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR) was studied. The sample was peach. Soluble solids content in peach was detected. The results shows influence of temperature on Vis/NIR spectra of the peach exists. The overall results sufficiently demonstrate that the performances of the same method, PLS, PCR or SMLR are similar, no matter what the data are at different temperatures.
In this study, the application potential of computer vision in on-line determination of CIE L*a*b* and content of
intramuscular fat (IMF) of pork was evaluated. Images of pork chop from 211 pig carcasses were captured while samples
were on a conveyor belt at the speed of 0.25 m•s-1 to simulate the on-line environment. CIE L*a*b* and IMF content
were measured with colorimeter and chemical extractor as reference. The KSW algorithm combined with region
selection was employed in eliminating the surrounding fat of longissimus dorsi muscle (MLD). RGB values of the pork
were counted and five methods were applied for transforming RGB values to CIE L*a*b* values. The region growing
algorithm with multiple seed points was applied to mask out the IMF pixels within the intensity corrected images. The
performances of the proposed algorithms were verified by comparing the measured reference values and the quality
characteristics obtained by image processing. MLD region of six samples could not be identified using the KSW
algorithm. Intensity nonuniformity of pork surface in the image can be eliminated efficiently, and IMF region of three
corrected images failed to be extracted. Given considerable variety of color and complexity of the pork surface, CIE L*,
a* and b* color of MLD could be predicted with correlation coefficients of 0.84, 0.54 and 0.47 respectively, and IMF
content could be determined with a correlation coefficient more than 0.70. The study demonstrated that it is feasible to
evaluate CIE L*a*b* values and IMF content on-line using computer vision.
The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids
contents (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for
NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. Multiplicative signal correcton
(MSC), the first and second derivative were applied for preprocessing spectral data. The relationship
between SSC, pH and FT-NIR spectra of tomato juice was analyzed via partial least-squares (PLS)
regression, respectively. PLS regression models for SSC and pH in tomato juices show the high accuracy.
The correlation coefficient (r), root mean square error of calibration (RMSEC) and root mean square error
of validation (RMSEP), root mean square error of cross-validation (RMSECV) for SSC were 0.91582,
0.0703, 0.150 and 0.138, respectively, whereas those values for pH were 0.8997, 0.0333, 0.0316 and 0.0489,
respectively. It is concluded that the NIR transmittance spectroscopy is promising for the fast and
nondestructive detection of chemical components in tomato juices.
KEYWORDS: Near infrared, Principal component analysis, Near infrared spectroscopy, Spectroscopy, Calibration, Statistical modeling, Data modeling, Statistical analysis, Optical filters, Oxidation
Near-infrared (NIR) transmittance spectroscopy combined with several chemometrical techniques was
investigated to study the oxidation process during storage in tomato juices. A total of 100 tomato juice
samples were used for NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. The
spectrum of each tomato juice was collected twice: the first time as soon as the tomatoes were squeezed,
centrifuged, filtered and the tomato juice had not undergone any oxidation process and the second
measurement was taken after a month. Principal component analysis (PCA) and partial least squares
discriminant analysis (PLSDA) were applied to discriminate between the two groups of spectra. The results
show that differences between tomato juices before and after the storage period do exist attributed to
changes in certain compounds of juice and excellent classification can be obtained after optimizing spectral
pretreatment.
White peach is a famous peach variety for its super-quality and high economic benefit. It is originally planted in Yuandong Villiage, Jinhua County, Zhejiang province. By now, it has been planted in many other places in southeast of China. However, peaches from different planting areas have dissimilar quality and taste, which result in different selling price. The objective of this research was to discriminate peaches from different planting areas by using near-infrared (NIR) spectra and chemometrics methods. Diffuse reflectance spectra were collected by a fiber spectrometer in the range of 800-2500 nm. Discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), and discriminant partial least square regression (DPLS) methods were employed to classify the peaches from three planting areas 'Jinhua', 'Wuyi', and 'Yongkang' of Zhejiang province. 360 samples were used in this study, 120 samples per planting area. The classifying correctness were above 92% for both DA and SIMCA mdoels. And the result of DPLS model was slightly better. By using DPLS method, two 'Jinhua' peaches, three 'Wuyi' peaches, and three 'Yongkang' peaches were misclassified, the accruacy was above 95%. The results of this study indicate that the three chemometrics methods DA, SIMCA, and DPLS are effective for discriminating peaches from different planting areas based on NIR spectroscopy.
Near infrared (NIR) spectroscopy is an ideal analytical method for rapid and nondestrctive measurement of the properties of agriculture products. The efficient use of this method is dependent on multivariate calibration methods determined by the sensitivity to variations. However, fluctuation of background and noise are unavoidable during collecting spectra, which will not only worsen the precision of prediction, but also complicate the multivariate models. Therefore, the first step of a multivariate calibration based on NIR spectra data is often to preprocess the data for the purpose of removing the varying background and noise. In this study, wavelet transform (WT) was used to eliminate the varying background and noise simultaneously in the near infrared spectroscopy signals of 55 navel oranges. Three families of mother wavelets (Symlets, Daubechies and Coiflet), four threshold selection rules (Rigrsure, Heursure, Minimaxi, Fixed form threshold), and two threshold functions (soft and hard) were applied to estimate the performances. The sugar content of intact navel orange was calculated by partial least squares regression (PLSR) with the reconstructed spectra after denoised. The results show that the best denoising performance was reached via the combination of Daubechies 5, "Fixed form" threshold selection rule, and hard threshold function. Based on the optimization parameter, wavelet regression models on sugar content in navel orange were also developed and resulted in a smaller prediction error than a traditional PLSR model.
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