Fast and Green Method to Control Frauds of Geographical Origin in Traded Cuttlefish Using a Portable Infrared Reflective Instrument

An appropriate seafood origin identification is essential for labelling regulation but also economic and ecological issues. Near infrared (NIRS) reflectance spectroscopy was employed to assess the origins of cuttlefish caught from five fishing FAO areas (Adriatic Sea, northeastern and eastern central Atlantic Oceans, and eastern Indian and western central Pacific Oceans). A total of 727 cuttlefishes of the family Sepiidae (Sepia officinalis and Sepiella inermis) were collected with a portable spectrophotometer (902–1680 nm) in a wholesale fish plant. NIR spectra were treated with standard normal variate, detrending, smoothing, and second derivative before performing chemometric approaches. The random forest feature selection procedure was executed to select the most significative wavelengths. The geographical origin classification models were constructed on the most informative bands, applying support vector machine (SVM) and nearest neighbors algorithms. The SVM showed the best performance of geographical classification through the hold-out validation according to the overall accuracy (0.92), balanced accuracy (from 0.83 to 1.00), sensitivity (from 0.67 to 1.00), and specificity (from 0.88 to 1.00). Thus, being one of the first studies on cuttlefish traceability using NIRS, the results suggest that this represents a rapid, green, and non-destructive method to support on-site, practical inspection to authenticate geographical origin and to contrast fraudulent activities of cuttlefish mislabeled as local.

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Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Applied Sciences ◽

10.3390/app10041520 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1520

Author(s):

Xiu Jin ◽

Shaowen Li ◽

Wu Zhang ◽

Juanjuan Zhu ◽

Jia Sun

Keyword(s):

Regression Models ◽

Near Infrared ◽

Potassium Concentration ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Standard Normal Variate ◽

Standard Normal ◽

Boosting Algorithms ◽

Near Infrared Ray

The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.

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Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽

10.1177/0960336020944007 ◽

2020 ◽

Vol 31 (5-6) ◽

pp. 25-29

Author(s):

Rita-Cindy Aye-Ayire Sedjoah ◽

Bangxing Han ◽

Hui Yan

Keyword(s):

Quality Control ◽

Near Infrared ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Analysis Model ◽

First Order ◽

Standard Normal Variate ◽

Standard Normal ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

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Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis

Journal of Spectral Imaging ◽

10.1255/jsi.2018.a8 ◽

2018 ◽

Author(s):

Priscila Aires ◽

Francisco Gambarra-Neto ◽

Wirton Coutinho ◽

Alderi Araujo ◽

Gilvan da Silva ◽

...

Keyword(s):

Pattern Recognition ◽

Near Infrared ◽

Intergenic Spacer ◽

Linear Discriminant ◽

Intergenic Spacer Region ◽

Standard Normal Variate ◽

Standard Normal ◽

Etiological Agents ◽

Speed Accuracy ◽

Non Destructive

Hyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. Five strains of CG and 46 strains of CGC were used. CG and CGC strains were grown on Czapek-agar medium at 25 °C under a 12-hour photoperiod for 15 days. Molecular identification was performed as a reference for the CG and CGC classes by polymerase chain reaction of the intergenic spacer region of rDNA. The scattering coefficient µs and the absorption coefficient µa were obtained, which resulted in a µs value for CG of 1.37 × 1019 and for CGC of 5.83 × 10–11. These results showed that the use of the standard normal variate was no longer essential and reduced the spectral range from 1000–2500 nm to 1000–1381 nm. The results evidenced two type II errors for the CG 457-2 and CGC 39 samples in the soft independent modelling model of the analogy model. There were no classification errors using the algorithm of the successive projections for variable selection in linear discriminant analysis (SPA-LDA). A parallel validation of the results obtained with SPA-LDA was performed using a box plot analysis with the 11 variables selected by SPA, in which there were no outliers for the HSI-NIR models. The new HSI-NIR and SPA-LDA procedures for the classification of CG and CGC etiological agents are noted for their greater analytical speed, accuracy, simplicity, lower cost and non-destructive nature.

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Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods

Foods ◽

10.3390/foods9121778 ◽

2020 ◽

Vol 9 (12) ◽

pp. 1778

Author(s):

Fan Wang ◽

Chunjiang Zhao ◽

Guijun Yang

Keyword(s):

Near Infrared ◽

External Validation ◽

Scatter Correction ◽

Nir Spectroscopy ◽

Spectral Ratio ◽

Optical Path Length ◽

Standard Normal Variate ◽

Length Estimation ◽

Standard Normal ◽

Non Destructive

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

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Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽

10.3390/pr9020316 ◽

2021 ◽

Vol 9 (2) ◽

pp. 316

Author(s):

Lakkana Pitak ◽

Kittipong Laloon ◽

Seree Wongpichet ◽

Panmanas Sirisomboon ◽

Jetsada Posom

Keyword(s):

Near Infrared ◽

Hyperspectral Image ◽

Thermal Conversion ◽

High Energy ◽

Volatile Matter ◽

Successive Projections Algorithm ◽

Color Distribution ◽

Standard Normal Variate ◽

Distribution Mapping ◽

Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

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On the Scales Associated with Near-Infrared Reflectance Difference Spectra

Applied Spectroscopy ◽

10.1366/0003702953964615 ◽

1995 ◽

Vol 49 (6) ◽

pp. 765-772 ◽

Cited By ~ 75

Author(s):

M. S. Dhanoa ◽

S. J. Lister ◽

R. J. Barnes

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

The Other ◽

Difference Spectra ◽

Near Infrared Reflectance ◽

Standard Normal Variate ◽

Near Infrared Spectra ◽

Standard Normal ◽

Relationship Of ◽

The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

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Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Applied Sciences ◽

10.3390/app10031173 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1173 ◽

Cited By ~ 2

Author(s):

Zhiqi Hong ◽

Yong He

Keyword(s):

Hyperspectral Imaging ◽

Nutritional Value ◽

Geographical Origin ◽

Principal Component ◽

Hyperspectral Images ◽

Imaging Systems ◽

Support Vector ◽

Spectral Ranges ◽

Tea Leaf ◽

Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

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Use of Difference near Infrared Reflectance Spectra to Extract Relevant Information from the Spectra of Agro–Food Products

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.125 ◽

1998 ◽

Vol 6 (1) ◽

pp. 89-95 ◽

Cited By ~ 14

Author(s):

Ana Garrido-Varo ◽

Ronald Carrete ◽

Víctor Fernández-Cabanás

Keyword(s):

Near Infrared ◽

Food Products ◽

Relevant Information ◽

Near Infrared Reflectance ◽

Standard Normal Variate ◽

Corn Gluten ◽

Corn Gluten Feed ◽

Standard Normal ◽

Gluten Feed ◽

By Products

This paper compares the use of log 1/ R versus standard normal variate (SNV) and Detrending (DT) transformations calculated either of two forms, SNV followed by DT (SNV+DT) or DT then SNV (DT+SNV) for their abilities to enhance interpretation of spectra and to detect areas of maximum differences in composition of two agro–food products (sunflower seed and corn) and their corresponding by-products (sunflower meal and corn gluten feed). The results obtained show that the SNV+DT and the DT+SNV transformations of the raw data make the existing chemical differences between scattering agro–food products more easily interpretable.

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Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

Applied Spectroscopy ◽

10.1177/0003702818777260 ◽

2018 ◽

Vol 72 (9) ◽

pp. 1362-1370 ◽

Cited By ~ 12

Author(s):

Hui Yan ◽

Heinz W. Siesler

Keyword(s):

Near Infrared ◽

Raw Materials ◽

Discrimination Performance ◽

Principal Component ◽

Identification Performance ◽

Score Plot ◽

Discrimination Capability ◽

Standard Normal Variate ◽

Correct Assignment ◽

Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

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