scholarly journals Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 55
Author(s):  
Sonia Nieto-Ortega ◽  
Ángela Melado-Herreros ◽  
Giuseppe Foti ◽  
Idoia Olabarrieta ◽  
Graciela Ramilo-Fernández ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Time Domain ◽  
Bioelectrical Impedance Analysis ◽  
Near Infrared ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Time Domain Reflectometry ◽  
Non Destructive

The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.

COMPARISON OF NEAR-INFRARED SPECTROSCOPY AND BIOELECTRICAL IMPEDANCE ANALYSIS FOR THE ASSESSMENT OF BODY COMPOSITION IN THE FRAIL ELDERLY

2014 ◽  
pp. 1-5
Author(s):  
T. KAMO ◽  
H. ISHII ◽  
D. TAKAHASHI ◽  
K. IWAGAYA ◽  
T. ISHIDA ◽  
...  
Keyword(s):  
Body Composition ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Frail Elderly ◽  
Bioelectrical Impedance Analysis ◽  
Near Infrared ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Community Dwelling ◽  
Composition Analysis

Background: Body composition is an important component of health related fitness. Near-infrared spectroscopy (NIRS) is a non-invasive, simple and rapid method of assessing body fat percentage. However, it is unknown whether NIRS can accurately estimate FFM in community-dwelling frail elderly. Objectives: This study aimed to compare NIRS with bioelectrical impedance analysis (BIA) in FFM measurement. Design: Cross-sectional study. Setting: Shizuoka, Japan. Participants: The study population comprised 53 community-dwelling frail elderly (15 men, 38 women; mean age 84.8±6.4 years; body mass index 19.7±3.5 kg/m2). Measurement: FFM and percentage fat mass (%FM) were estimated using a NIRS device at two sites (biceps and calf) and compared to body composition measured by BIA. Simple linear regression and Bland–Altman analyses were used to determine agreement between the methods. Results: FFM determined by BIA highly correlated with that determined by NIRS at both the biceps and calf (r=0.92 for both; p<0.001). The correlation coefficients for %FM estimated by NIRS were slightly lower (r=0.70 for biceps; r=0.66 for calf). In NIRS assessments, systematic biases were found for %FM but not for FFM. Conclusion: NIRS has significant potential for body composition analysis. Further comparative and longitudinal studies need to be conducted using an agreed reference analysis method to find a simple and more suitable method that can be applied among the community-dwelling frail elderly.

scholarly journals Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

2021 ◽  
Author(s):  
Pedro N.S. Sampaio ◽  
Carla Brites
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Biochemical Properties ◽  
Agricultural Materials ◽  
Non Destructive

Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.

scholarly journals Research Note: Non-destructive Detection of Super Grade Chick Embryos or Hatchlings using Near-infrared Spectroscopy

2021 ◽  
pp. 101189
Author(s):  
Alin Khaliduzzaman ◽  
Ayuko Kashimori ◽  
Tetsuhito Suzuki ◽  
Yuichi Ogawa ◽  
Naoshi Kondo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Research Note ◽  
Chick Embryos ◽  
Non Destructive

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

Fast Non-Destructive Detection of Wool Content in Blended Fabrics Based on Near-Infrared Spectroscopy Technology

2015 ◽  
Vol 671 ◽  
pp. 356-362 ◽  
Author(s):  
Zhi Feng Chen ◽  
Yuan Quan Hong ◽  
Chang Jiang Wan ◽  
Lian Ying Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Detection Method ◽  
Quantitative Model ◽  
National Standard ◽  
Fast Detection ◽  
Significant Difference ◽  
Validation Set ◽  
Non Destructive

A fast non-destructive method of detection of wool content in blended fabrics was studied based on Near Infrared spectroscopy technology in order to avoid the time-consuming, tedious work and the destruction of samples in the traditional inspection. 621 wool/nylon, wool/polyester and wool/nylon/polyester blended fabrics were taken as research objects. To get the wool content, we established the wool near-infrared quantitative model by partial least squares (PLS) method after analyzing the color and composition of the samples. For verifying the validity and practicability of the model, 100 samples were chosen as an independent validation set. The variance analysis shows that there is no significant difference between Near Infrared fast detection method and national standard method (GB/T2910-2009),which indicates that this method is expected to be a means of fast non-destructive detection and will have extensive application future in the field of wool content detection.

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