scholarly journals Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019 ◽  
Vol 9 (18) ◽  
pp. 3926 ◽  
Author(s):  
Yue Zhang ◽  
Hongzhe Jiang ◽  
Wei Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chicken Meat ◽  
Regions Of Interest ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Plsr Model ◽  
Volume Range

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

scholarly journals Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

2015 ◽  
Vol 24 (2) ◽  
pp. eRC03 ◽  
Author(s):  
António J.A. Santos ◽  
Ofélia Anjos ◽  
Helena Pereira
Keyword(s):  
Kraft Pulp ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Present Model ◽  
Nir Spectroscopy ◽  
Kraft Pulps ◽  
Least Squares Regression ◽  
Acacia Melanoxylon ◽  
Pls Analysis ◽  
Plsr Model

<p><em>Aim of the study:</em> The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of <em>Acacia melanoxylon</em> R. Br.</p><p><em>Area of study: </em>Sites covering littoral north, mid interior north and centre interior of Portugal.</p><p><em>Materials and methods:</em> The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %.</p><p><em>Main results:</em> Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm<sup>-1</sup> and 4605 to 4243 cm<sup>-1</sup> allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP), a r<sup>2</sup> of 99.5 % with a RPD of 14.7.</p><p><em>Research highlights:</em> According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8).</p><p class="BioresourcesKeywords"><strong>Key words:</strong>  Acacia melanoxylon;<em> unbleached Kraft pulps; ISO Brightness; NIR; RPD.</em></p>

Prediction of Water Activity in Mamón (Filipino sponge) Cakes by near Infrared Hyperspectral Imaging

2020 ◽  
Vol 862 ◽  
pp. 7-11
Author(s):  
Manunchaya Sricharoonratana ◽  
Sontisuk Teerachaichayut
Keyword(s):  
Hyperspectral Imaging ◽  
Water Activity ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
The Philippines ◽  
Activity Measurement ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Physical And Chemical ◽  
Non Destructive

Water activity in foods can result in detrimental microbial activity during storage. The usual methods of water activity measurement involve destruction of the sample. Near infrared (NIR) hyperspectral imaging has previously been successfully used as a non-destructive method to determine various physical and chemical characteristics of a variety of foods. Therefore, this method was tested to determine whether it could be used to measure water activity of mamón cakes, a popular sponge cake developed in the Philippines. Individual samples (n = 178) were divided into a calibration set (n=119) and a prediction set (n=59). These samples were tested using NIR hyperspectral imaging (935-1720 nm) with a smoothing spectral pretreatment selected for developing the calibration model. Partial least squares regression was used to establish the model in order to predict the water activity. The results showed the accuracy of the calibration model in prediction that gave a correlation coefficient of 0.767 and the root mean square error of prediction of 0.0130. It was therefore concluded that NIR hyperspectral imaging has a potential for use and application for measuring the water activity of mamón cakes.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

scholarly journals Wood species identification from Atlantic forest by near infrared spectroscopy

2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Atlantic Forest ◽  
Wood Species ◽  
Native Species ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Planted Forests ◽  
Good Ability

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.

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