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

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.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033516686041 ◽

2017 ◽

Vol 25 (1) ◽

pp. 63-71 ◽

Cited By ~ 5

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.

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

Applied Sciences ◽

10.3390/app112311379 ◽

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.

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

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.1050 ◽

2013 ◽

Vol 21 (3) ◽

pp. 195-202 ◽

Cited By ~ 5

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

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

Remote Sensing ◽

10.3390/rs13061128 ◽

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.

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Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

Journal of Analytical Methods in Chemistry ◽

10.1155/2015/752162 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 15

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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Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518821833 ◽

2019 ◽

Vol 27 (1) ◽

pp. 75-85 ◽

Cited By ~ 1

Author(s):

Lorenzo Serva ◽

Stefania Balzan ◽

Vittoria Bisutti ◽

Filomena Montemurro ◽

Giorgio Marchesini ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Shelf Life ◽

Near Infrared Spectroscopy ◽

Storage Time ◽

Apple Juice ◽

Near Infrared ◽

Partial Least Square ◽

Feasibility Trial ◽

End Point

Fresh products, such as cloudy apple juice, could be preserved from early spoilage through the application of non-thermal processes such as sonication. However, shelf-life analyses based on microbiological and sensory evaluations are expensive and time consuming. Few studies have applied near infrared spectroscopy to evaluate the quality and decay of apple juices. Here, a feasibility trial was conducted to study the spectral behaviour at 1300–2500 nm combined with chemometric approaches. The shelf-life was monitored during two experiments, a challenge test with juices inoculated with spoilage yeasts (inoculated non-sonicated (INS)) and then submitted to sonication treatments (inoculated sonicated (IS)), and a storage test to evaluate the spoilage on non-inoculated juices (non-inoculated non-sonicated (NINS)) and sonicated non-inoculated juices (non-inoculated sonicated (NIS)). These experiments were investigated at six different refrigeration times 7, 14, 21, 28 and 60 days. Two functions were modelled to describe the behaviours of the first principal component according to the storage time. In agreement with a previous chemical and sensory evaluation, this approach allowed us to highlight shelf-life end points of 7 and 14 days for non-sonicated and sonicated samples, respectively. Three different models were evaluated for classification purposes: (1) sonicated versus non-treated samples, (2) end-point shelf-life evaluation at seven days for the NINS and INS juices and (3) end-point shelf-life discrimination at 14 days for IS and NIS samples. A partial least square-discriminant analysis enabled a group classification with accuracy values ranging from 0.63 to 1.00. The application of a variable importance in projection index to interpret the wavelengths of the spectral features suggests a contribution of organic acids and lipids to the prediction of decay. A canonical discriminant analysis provided a clearer separation of samples according to the storage time, especially in relation to the two time thresholds of 7 and 14 days.

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Using near infrared spectroscopy to determine moisture and starch content of corn processing products

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517728146 ◽

2017 ◽

Vol 25 (5) ◽

pp. 348-359 ◽

Cited By ~ 2

Author(s):

Ye Chen ◽

Lauren Delaney ◽

Susan Johnson ◽

Paige Wendland ◽

Rogerio Prata

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Starch Content ◽

Rapid Development ◽

Partial Least Square ◽

Least Square ◽

Operational Parameters ◽

Corn Flour ◽

Analytical Technique

Due to the rapid development of the corn-to-ethanol industry, the demand for process monitoring has led to the gradual substitution of traditional analytical techniques with fast and non-destructive methods such as near infrared spectroscopy. In this study, the feasibility of using Fourier transform–near infrared technology as an analytical tool to predict operational parameters (dry solids, starch, carbohydrate, and ethanol content) was investigated. Corn flour, liquefied mash, fermented mash, and distiller’s dried grains with solubles were selected to represent the feedstock, two intermediate products, and one primary co-product of corn-to-ethanol plants, respectively. Multivariate partial least square calibration models were developed to correlate near infrared spectra to the corresponding analytical values. The validation results indicate that near infrared models can be developed that will predict various parameters accurately (root mean square error of prediction: 0.16–1.14%, residual predictive deviation: 3.0–6.3). Measurement of starch or carbohydrate content in corn flour or distiller’s dried grains with solubles is challenging due to low accuracy of wet chemistry methods as well as sample complexity. The study demonstrated that near infrared spectroscopy, a high-throughput analytical technique, has the potential to replace the enzymatic assay.

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Noninvasive Alcohol Testing Using Near-Infrared Spectroscopy and Partial Least Square Method

2012 Symposium on Photonics and Optoelectronics ◽

10.1109/sopo.2012.6270460 ◽

2012 ◽

Author(s):

Guoqiang Chen ◽

Site Luo ◽

Zengyong Li

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Least Square Method ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Method ◽

Alcohol Testing

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Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy

10.1101/533802 ◽

2019 ◽

Author(s):

Marta F. Maia ◽

Melissa Kapulu ◽

Michelle Muthui ◽

Martin G. Wagah ◽

Heather M. Ferguson ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Anopheles Gambiae ◽

Near Infrared Spectroscopy ◽

Large Scale ◽

Near Infrared ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Human Malaria ◽

The Impact

AbstractLarge-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

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