Classification of fish meal produced in China and Peru by online near infrared spectroscopy with characteristic wavelength variables

2017 ◽  
Vol 25 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Guanghui Shen ◽  
Lujia Han ◽  
Xia Fan ◽  
Xian Liu ◽  
Yaoyao Cao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fish Meal ◽  
Partial Least Square ◽  
Least Square ◽  
Analysis Model ◽  
Characteristic Wavelength ◽  
Validation Set

Online near infrared reflectance spectroscopy combined with characteristic wavelength variables was used to establish a fast and nondestructive analytical method for the classification of fish meal produced in China and Peru. In this study, 117 fish meal samples (47 from China and 70 from Peru) were scanned in the spectral range of 1000–2500 nm by the online near infrared spectroscopy instrument applied on the conveyor belt. The K–S (Kennard–Stone) method was used for the division of samples into calibration and validation sets. Principal component analysis and partial least square discriminant analysis were applied to classify fish meal samples. The results showed that the discrimination accuracies with calibration and validation set samples were 100% and 89.74%, respectively, for the partial least square discriminant analysis model using the full spectrum after the optimimal spectral pre-treatment. Then competitive adaptive reweighted sampling (CARS) was used to select the characteristic wavelength variables for partial least square discriminant analysis model analysis, and the discrimination accuracy for the validation set increased to 94.87%. All the results indicated that online near infrared spectroscopy combined with characteristic wavelength variables could be used for discriminating fish meal samples produced in different places, which offers feed purchasers an effective, reliable, and real-time analysis method for the identification and authentication of the commercial fish meal product.

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

Application of Near Infrared Spectroscopy in the Analysis of Printing Color Detection

2012 ◽  
Vol 262 ◽  
pp. 59-64
Author(s):  
Hong Wei Lu ◽  
Hong He ◽  
Jun Ji ◽  
Guo Qiang Liu ◽  
Ying Hu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Color Parameters ◽  
Detection Model ◽  
Color Detection ◽  
Validation Set

For the fast and exact detection of printing color, we combine the near infrared (NIR) spectroscopy technique with partial least square (PLS) to build the detection model of printing color. Applying the 144 samples of spectral curve which obtained by the near infrared spectroscopy randomly separated into calibration set and validation set, and base on the 120 calibration set data to establish the prediction model of printing color by PLS, then this model is employed for predicting the color of the 24 validation set. The RMSEC of the 24 blocks’ color parameters L*, a*, b*, E are 0.73, 2.26, 3.03 and 1.09 respectively; The RMSEP are 0.97, 2.77, 3.57 and 1.34 respectively. Those results tell that the NIR spectrum and blocks’ color parameters L*, a*, b*, E could accurately establish a quantitative regression model, applying such model also can accurately predict unknown samples, and the results approximate to the original reference data. The use of near infrared spectroscopy to detect the printed matter nondestructively is feasible, and lays the foundation for the further analysis and establishment of printing chroma model.

Quantitative Analysis of Radix Rehmanniae from Genuine Producing Area by Near Infrared Spectroscopy

2014 ◽  
Vol 912-914 ◽  
pp. 2010-2013
Author(s):  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Ming Xia Wu ◽  
Yong Xia Cui ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Water Extract ◽  
Partial Least Square ◽  
Water Soluble ◽  
Least Square ◽  
Analysis Model ◽  
Soluble Components

In this paper the water extract of Radix Rehmanniae from genuine producing area was rapidly determined by near-infrared spectroscopy (NIRS). The quantitative analysis model of the water extraction of Radix Rehmanniae was established by Partial Least Square (PLS). The correlation coefficient of calibration (R2) was 0.99529; the root-mean-square error of prediction (RMSEP) was 0.134. The results indicated that the water-soluble components in Radix Rehmanniae from genuine producing area can be rapidly and accurately determined by near-infrared spectroscopy technology.

Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee

2016 ◽  
Vol 121 ◽  
pp. 313-319 ◽  
Author(s):  
Izabele Marquetti ◽  
Jade Varaschim Link ◽  
André Luis Guimarães Lemes ◽  
Maria Brígida dos Santos Scholz ◽  
Patrícia Valderrama ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Arabica Coffee

scholarly journals Potencial Use of Near Infrared Spectroscopy (NIRS) to Categorise Chorizo Sausages from Iberian Pigs According to Several Quality Standards

2021 ◽  
Vol 11 (23) ◽  
pp. 11379
Author(s):  
Alberto Ortiz ◽  
Lucía León ◽  
Rebeca Contador ◽  
David Tejerina
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Raw Material ◽  
Linear Discriminant ◽  
Discriminant Ability

The ability of Near Infrared Spectroscopy (NIRS) to classify pre-sliced Iberian chorizo modified atmosphere packaged (MAP) according to the animal material used in their production (Black, Red, White) in their production in accordance with the official trade categories (which includes the handling system and the different inter-racial crossbreeds) without opening the package was assayed. Furthermore, various spectra pre-treatments and supervised classification chemometric tools; Partial least square-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and linear discriminant analysis (LDA), were assessed. The highest sensitivity values in both calibration and external validation were achieved with SIMCA followed by PLS-DA approaches, while LDA had more provided values among sensitivity and specificity and between the different commercial categories in both sample sets, thus yielding the highest discriminant ability. These results could be a resource to support the traceability and authentication control of individual pre-sliced MAP Iberian chorizo according to the commercial category of the raw material in a non-destructive way.

scholarly journals Rapid quantification of epigoitrin in the extraction process of Radix Isatidis using near infrared spectroscopy

2021 ◽  
Vol 233 ◽  
pp. 02020
Author(s):  
Caixia Li ◽  
Wenjing Huang ◽  
Qian Wang ◽  
Zhiwei Huang ◽  
Shikai Yan
Keyword(s):  
Infrared Spectroscopy ◽  
Process Control ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Extraction Process ◽  
Partial Least Square ◽  
Least Square ◽  
Efficient Technology ◽  
Radix Isatidis ◽  
Validation Set

Near-infrared Spectroscopy (NIR) is widely accepted as an efficient technology for process control in the production of traditional Chinese medicine (TCM). This study was to establish a NIR-based approach to determining epigoitrin of Radix Isatidis during temperature-controlled extraction process. 86 extracts of Radix Isatidis were prepared in 50 °C water for 4 hours, and were randomly divided into validation set and calibration set. The concentration of epigoitrin of each sample was determined by HPLC/UV, and correspondingly NIR spectra of those samples were also acquired. Partial least square (PLS) algorithm was utilized to develop a predictive model on NIR spectra data and contents of epigoitrin in samples of calibration set. The model displays good performance with acceptable values of SECV, SEC, LV and R2, and it was applied to predict the concentration of epigoitrin in samples of validation set from their NIR data. As a result, the model produced accurate result with little deviation between predicted values and experimental values. The proposed NIR method is expected to be developed as a promising approach for process control in TCM production.

Partial Least Square Discriminant Analysis of Mangosteen Pericarp Powder by near Infrared Spectroscopy

2013 ◽  
Vol 21 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Jomjai Peerapattana ◽  
Hideyuki Shinzawa ◽  
Kuniko Otsuka ◽  
Yusuke Hattori ◽  
Makoto Otsuka
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square

Measurement of Titratable Acidity in Gannan Navel Orange by near Infrared Spectroscopy

2013 ◽  
Vol 781-784 ◽  
pp. 1485-1488
Author(s):  
Yu Bo Liao ◽  
Long Sheng Huang ◽  
Wen Xia Yang ◽  
Liang Liao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Titratable Acidity ◽  
Least Square Method ◽  
Partial Least Square ◽  
Least Square ◽  
Navel Orange ◽  
Validation Set ◽  
Gannan Navel Orange

To explore a rapid and non-destructive method for measuring titratable acidity in Gannan Navel Orange, near infrared spectroscopy combined with partial least square method was adopted for building a prediction model. Multiple statistical analysis results show that when the optimal spectral region (950-1330nm) was selected for modeling, the correlation coefficient of the validation set is 0.9085, and RMSECV is 0.0243g/100g. This technique using near infrared spectroscopy is rapid, convenient and nondestructive, and may potentially become a strong tool for the quality evaluation and fruit sorting of navel oranges.

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

scholarly journals Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Meng ◽  
Shisheng Wang ◽  
Rui Cai ◽  
Bohai Jiang ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Traditional Chinese Herbal Medicine ◽  
Total Alkaloids ◽  
Rapid Discrimination ◽  
Different Origins

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

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