scholarly journals Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dongdong Li ◽  
Yaling Peng ◽  
Haihong Zhang
Keyword(s):  
Near Infrared ◽  
Successive Projections Algorithm ◽  
Classification Rate ◽  
Full Spectrum ◽  
Original Spectrum ◽  
Standard Normal Variate ◽  
Scattering Correction ◽  
Standard Normal ◽  
Validation Set

To study the texture, microstructural changes, and classification of cold-fresh (C-F), freeze-thawed once (F-T0), and freeze-thawed twice Tan mutton (F-Tt), the aforementioned three types of Tan mutton were subjected to near-infrared hyperspectrum scanning, scanning electron microscopy, and TPA testing. The original spectrum of Tan mutton was obtained at a wavelength range of 900∼1,700 nm after hyperspectrum scanning; a spectrum fragment ranging from 918 nm to 1,008 nm was intercepted, and the remaining original spectrum was used as a studied spectrum (“full spectrum” hereafter). The full spectrum was pretreated by SNV (standard normal variate), MSC (multiple scattering correction), and SNV + MSC and then extracted feature wavelengths by SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) algorithm, and 25 feature wavelengths were obtained. By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. In contrast, SNV + MSC−CARS−PLS-DA yielded the highest classification rate of 98% and 100% for calibration set and validation set, respectively. The results indicated that the texture and surface microstructure of F-T Tan mutton deteriorated, and more worsely with F-T time. SNV+MSC-CARS-PLS-DA could be well used to classify C-F, F-T0, and F-Tt Tan mutton.

Classification of Floral Origins of Honey by NIR and Chemometrics

2012 ◽  
Vol 605-607 ◽  
pp. 905-909 ◽  
Author(s):  
Xiu Ying Liang ◽  
Xiao Yu Li ◽  
Wen Jun Wu
Keyword(s):  
Spectral Data ◽  
Spectral Region ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Scattering Correction ◽  
Standard Normal

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been investigated to discriminate type of honey. 147 NIR spectra of six floral origins of honey samples were collected within 4000~10000cm-1 spectral region. Spectral data were compressed using partial least squares (PLS). Back propagation neural networks (BPNN) models were constructed to distinguish the type of honey. Six spectral data pretreatments including first derivative, first derivatives followed by mean centering(MC), second derivatives, Savitzky-Golay smoothing, standard normal variate transformation (SNV) and multiplicative scattering correction (MSC) were compared to establish the optimal models for honey discrimination. Savitzky-Golay smoothing proved more effective than the other data pretreatments. BPNN models were developed within the full spectral region, 5303～6591cm-1 and 7012～10001cm-1, respectively. Results have shown that the highest(100%) classification rate was achieved within 5303～6591cm-1 wave range. Our results indicated that NIR spectroscopy with chemometrics techniques can be applied to classify rapidly honeys of different floral origin.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

Use of Difference near Infrared Reflectance Spectra to Extract Relevant Information from the Spectra of Agro–Food Products

1998 ◽  
Vol 6 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Ana Garrido-Varo ◽  
Ronald Carrete ◽  
Víctor Fernández-Cabanás
Keyword(s):  
Near Infrared ◽  
Food Products ◽  
Relevant Information ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Corn Gluten ◽  
Corn Gluten Feed ◽  
Standard Normal ◽  
Gluten Feed ◽  
By Products

This paper compares the use of log 1/ R versus standard normal variate (SNV) and Detrending (DT) transformations calculated either of two forms, SNV followed by DT (SNV+DT) or DT then SNV (DT+SNV) for their abilities to enhance interpretation of spectra and to detect areas of maximum differences in composition of two agro–food products (sunflower seed and corn) and their corresponding by-products (sunflower meal and corn gluten feed). The results obtained show that the SNV+DT and the DT+SNV transformations of the raw data make the existing chemical differences between scattering agro–food products more easily interpretable.

SPXY Sample Classification Method and Successive Projections Algorithm Combined with Near-Infrared Spectroscopy for the Determination of Total Sugar Content of Southern Xinjiang Jujube

2014 ◽  
Vol 1030-1032 ◽  
pp. 352-356 ◽  
Author(s):  
Yun Fa Peng ◽  
Hua Ping Luo ◽  
Xue Ning Luo ◽  
Ying Zhan
Keyword(s):  
Near Infrared ◽  
Sugar Content ◽  
Total Sugar ◽  
Internal Quality ◽  
Successive Projections Algorithm ◽  
Full Spectrum ◽  
Important Indicator ◽  
Characteristic Wavelength ◽  
Successive Projections ◽  
Southern Xinjiang

Sugar degree is an important indicator of red jujube internal quality. The main objectives of this paper are to minimize the collinearity between spectral variables, to find the variable groups which containing the lowest redundant information,and establish the model with better robustness by means of fewer variables. This paper uses SPXY (sample set partitioning based on joint x-y distances) to divide calibrating samples,and applies successive projections algorithm (SPA) to select the near-infrared spectral characteristic variable of southern Xinjiang jujube total sugar. To further establish the partial least squares (PLS) model with selected variables. The root mean square error of prediction (RMSEP) of the model is 2.8804. The correlation coefficient of prediction r is 0.9005.To compare the established PLS model results between SPA selecting variables and full spectrum. The results showed that: Firstly, the divided calibrating samples is reasonable in SPXY way.Secondly, SPA optimizes 9 variables of the full spectrum 1557 variables,and prediction effect of the established PLS model is better than the full spectrum PLS model.Finally,SPA can effectively select characteristic wavelength of component under test.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

Process Evaluation of Key Parameters during Plant-Field Composting Using Genetic Algorithms and Near-Infrared Spectroscopy

2012 ◽  
Vol 532-533 ◽  
pp. 202-207
Author(s):  
Guang Qun Huang ◽  
Lu Jia Han ◽  
Xiao Yan Wang
Keyword(s):  
Genetic Algorithms ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Electronic Conductivity ◽  
Chicken Manure ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Full Spectrum ◽  
Validation Set

The nondestructive estimation of key parameters during plant-field chicken manure composting is of great importance for quality evaluation. In the process of developing regression models using near-infrared spectroscopy (NIRS), methods used for wavelength selection significantly influence on the efficiency of the calibration. This study explored the method of genetic algorithms (GAs) for selecting highly related wavelengths to improve NIRS models for moisture (Miost), pH and electronic conductivity (EC), total carbon (TC), total nitrogen (TN) and C/N ratio determination in chicken manure during composting. Based on the values of coefficient of determination in the validation set (R2) and root mean square error of prediction (RMSEP), the prediction results were evaluated as excellent for Miost, TC and TN, good for pH and EC, and approximate for C/N ratio. But GAs had better performance than using full spectrum for near-infrared spectroscopy model construction in the process of evaluating key parameters during plant-field chicken manure composting.

scholarly journals Feasibility Study on Use of Near Infrared Spectroscopy for Rapid and Non-Destructive Determination of Gossypol Content in Intact Cottonseeds

2020 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Gossypol Content ◽  
Mean Square ◽  
Standard Normal Variate ◽  
Standard Normal

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05–0.07, 0.04–0.06, 0.82–0.92, and 2.3–3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

2019 ◽  
Vol 82 (5) ◽  
pp. 796-803
Author(s):  
R. PUTTHANG ◽  
P. SIRISOMBOON ◽  
C. DACHOUPAKAN SIRISOMBOON
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Aflatoxin B1 ◽  
Rapid Detection ◽  
Near Infrared ◽  
Cross Validation ◽  
Polished Rice ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Rice Samples

ABSTRACT The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B1 (AFB1) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB1 and 15 samples artificially contaminated with AFB1. Quantitative calibration models to detect AFB1 were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of −0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB1 contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB1 contamination.

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