scholarly journals Assessment of the Analytical Performance of Three Near-Infrared Spectroscopy Instruments (Benchtop, Handheld and Portable) through the Investigation of Coriander Seed Authenticity

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 956
Author(s):  
Claire McVey ◽  
Una Gordon ◽  
Simon A. Haughey ◽  
Christopher T. Elliott
Keyword(s):  
Regression Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Regression Models ◽  
Near Infrared ◽  
Prediction Models ◽  
Screening Tools ◽  
Food Supply Chain ◽  
Site Analysis ◽  
Coriander Seed

The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.

scholarly journals Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Suk-Ju Hong ◽  
Shin-Joung Rho ◽  
Ah-Yeong Lee ◽  
Heesoo Park ◽  
Jinshi Cui ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Seed Oil ◽  
Prediction Models ◽  
Oxidation Process ◽  
Analysis Techniques ◽  
Peroxide Values ◽  
Perilla Seed

Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

scholarly journals Near-infrared spectroscopy used to predict soybean seed germination and vigour

2018 ◽  
Vol 28 (3) ◽  
pp. 245-252 ◽  
Author(s):  
Maythem Al-Amery ◽  
Robert L. Geneve ◽  
Mauricio F. Sanches ◽  
Paul R. Armstrong ◽  
Elizabeth B. Maghirang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Seed Germination ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Soybean Seed ◽  
Seed Vigour ◽  
Near Infrared Spectra ◽  
High Germination ◽  
Qualitative Models

AbstractRapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.

Near infrared spectroscopy to evaluate change in color and chemical composition in heat-treated bamboo

2020 ◽  
Vol 28 (4) ◽  
pp. 214-223
Author(s):  
Junqian Mo ◽  
Wenbo Zhang ◽  
Xiaohui Fu ◽  
Wei Lu
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Chemical Components ◽  
Outer Side ◽  
Heating Process ◽  
Least Squares Regression ◽  
Heat Treated

This study investigated the feasibility of using near infrared spectroscopy technology to predict color and chemical composition in the heat-treated bamboo processing industry. The quantitative presentations of the changes in the chemical components were discussed using the difference spectra method of the 2nd derivative NIR spectra of the heat-treated bamboo samples. Then, the relationships between the color changes of the heat-treated bamboo and its near infrared spectra were constructed using the changes in the chemical components of the bamboo samples during the heating process. The prediction of color and chemical composition of both the outer and inner sides of the heat-treated bamboo surface were constructed using partial least squares regression method combined with a leave-one-out cross-validation process. Then, the results were validated by independent sample sets. The proposed prediction models were found to produce high r2P (above 0.93), RPD (above 3.13), and low RMSEP for both the outer and inner sides of the heat-treated bamboo samples. These studies’ results confirmed that the proposed models, especially outer side models, were perfectly suitable for the in-process inspections of the color and chemical content changes of heat-treated bamboo.

scholarly journals Agricultural products quality determination by means of near infrared spectroscopy

2022 ◽  
Vol 951 (1) ◽  
pp. 012112
Author(s):  
A A Munawar ◽  
Z Zulfahrizal ◽  
R Hayati ◽  
Syahrul
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Agricultural Products ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Non Destructive

Abstract Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.

scholarly journals Near infrared spectroscopy for assessing mechanical properties of Castanea sativa wood samples

2019 ◽  
Vol 50 (4) ◽  
pp. 191-197 ◽  
Author(s):  
Manuela Mancini ◽  
Elena Leoni ◽  
Michela Nocetti ◽  
Carlo Urbinati ◽  
Daniele Duca ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Bending Strength ◽  
Prediction Models ◽  
Castanea Sativa ◽  
Modulus Of Rupture ◽  
Current Standard ◽  
Standard Samples

Near infrared spectroscopy (NIR) is a technique widely used for the prediction of different chemical-physical features of wood. In this study, the technique was used to assess its potential to predict the mechanical characteristics of wood. Castanea sativa samples of three different European provenances were collected and laboratory tests were performed to assess the mechanical properties of wood samples. Modulus of elasticity (MOE), load-deflection curve and modulus of rupture (MOR) were calculated by using INSTRON machine with three points bending strength with elastic modulus, while density (D) was calculated according to the current standard. Samples were then analysed by means of NIR spectroscopy. The raw spectra were pre-processed and regression models were developed. Variables selection techniques were used to improve the model performance. In detail, MOE regression model returned an error of 696.01 MPa (R2=0.78). Instead, MOR and D prediction models must be further investigated on a wider number of samples considering the high variability in physical characteristics of chestnut wood. The results demonstrated the possibility to use NIR technique for the prediction of the mechanical properties of wood providing useful indications in evaluation-screening processes. Indeed, the presence of the principal wood compounds (cellulose, hemicellulose and lignin) and their influence in the characterisation of mechanical stress reactions were confirmed.

Use of LOCAL algorithm with near infrared spectroscopy in forage resources for grazing systems in Colombia

2017 ◽  
Vol 26 (1) ◽  
pp. 44-52 ◽  
Author(s):  
C Ariza-Nieto ◽  
OL Mayorga ◽  
B Mojica ◽  
D Parra ◽  
G Afanador-Tellez
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Local Algorithm ◽  
Forage Plants ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Global And Local

This study used a total of 2020 Colombian forage resources of three families (Grass forages, legume forages, and other forage plants) to develop near infrared spectroscopy calibrations for predicting the nutritional value. Spectra were collected at 2 nm increments using a scanning visible/near infrared spectrometer. The reference data used for each forage were crude protein, crude ash, neutral detergent fiber, acid detergent fiber, acid detergent lignin, measured according to the Association of Official Analytical Chemists. Two chemometric tools for developing near infrared spectroscopy prediction models were compared: the GLOBAL modified partial least squares, and the calibration strategy known as LOCAL. The LOCAL procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples were used as calibration sets after one-tenth of the samples were selected for validation from each database. Predictions of nutrition indicators in validation samples using generic and specific calibrations were compared with both GLOBAL and LOCAL procedures. For all predicted forages, LOCAL resulted in a significant improvement in both standard error of prediction and bias values compared with GLOBAL. Determination coefficient values (r2) also improved using the LOCAL algorithm, exceeding 0.9 for most forage sets. LOCAL calibration was then used with only one database (n 2020) comprising all the forage samples and SEP and r2 were similar to those obtained in the three databases using LOCAL algorithm. Therefore, LOCAL can accurately predict the composition of different forages using only one database, and could offer a practical way to develop robust equations taking into account the biodiversity of Colombian forages.

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