A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages, sex and color by means of visible and near infrared reflectance spectroscopy

Canaza-Cayo, Ali William; Cozzolino, Daniel; Alomar, Daniel; Quispe, Edgar

doi:10.1016/j.compag.2012.07.013

Cited by 13 publications

(12 citation statements)

References 25 publications

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“…Also, a multilayer perceptron was utilized as ANN classification method [ 29 ]. Partial least squares discriminant analysis (PLS-DA) has been extensively applied for classification problems, such as strawberry varieties [ 12 ], cooked ham quality [ 30 ], detection of expired vacuum-packed smoked salmon [ 31 ], Alpaca wool samples [ 32 ] or olive varietal identification [ 33 ]. PLS-DA expects to find a proper correlation of spectral variations and a set of defined classes.…”

Section: Methodsmentioning

confidence: 99%

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

et al. 2015

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The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network’s modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

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Section: Methodsmentioning

confidence: 99%

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

et al. 2015

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“…However, Alomar et al and Canaza-Cayo et al obtained lower values (SECV = 1.24 µm and 1.01 µm, respectively) [13,28]. The value of RPD = 1.91, close to 2, means that models obtained could be implemented for ranking purposes in selection and breeding programmes in llama herds [29,44].…”

Section: Spectral Pretreatments and Calibration Modelsmentioning

confidence: 90%

“…The predictive performance of each model was evaluated on the basis of the following statistical variables: coefficient of determination (R 2 ), standard error of cross validation (SECV), standard error of external validation (SEV) and relative predictive determinant (RPD). RPD indicates model accuracy and represents the ratio between standard deviation of the reference analysis and SECV [27,44]. In order to select the best calibration models, the RPD value greater than three has been considered adequate for analytical purposes.…”

Section: Spectral Preprocessing and Calibration Models Developmentmentioning

confidence: 99%

Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Amorena

Álvarez

Fernández-Ahumada

2021

Animals

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Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

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“…In addition, a stronger absorbance was seen at a higher wavenumber, approaching 10,000 cm −1 . In NIR spectroscopy, it is known that a dark sample absorbs higher wavenumber vibrations [24]. Therefore, this demonstrated that the dark colored sample became more highly digestible by cellulase.…”

Section: Interpretation Of Calibration Model From Solid Analysis For mentioning

confidence: 97%

Line monitoring by near-infrared chemometric technique for potential ethanol production from hydrothermally treated Eucalyptus globulus

Horikawa

Imai

Kanai

et al. 2015

Biochemical Engineering Journal

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a b s t r a c tThis study reports a method that combines near-infrared (NIR) measurements with multivariate analysis to predict the saccharification efficiency of hydrothermally pretreated Eucalyptus globulus during ethanol conversion. Optimization of the NIR data with or without spectral treatment determined the best calibration model in the region 10000-4000 cm −1 of the original spectra, with an RMSEP of 2.08% and R p 2 of 0.99. By investigating the regression coefficient to understand the key regions and chemical components, for original and multiplicative scatter correction (MSC)-treated spectra, the water absorption and higher wavenumber regions were important. For the second derivative spectra, the regression model was constructed based on the CH overtone vibrations (6050-5500 cm −1 ). The regression coefficient demonstrated that the removal of hemicellulose resulted in higher lignin content, which might affect the biomass properties in terms of water absorption and enhanced enzymatic hydrolysis evaluated by dinitrosalicylic acid (DNS) method. For a higher throughput system, aqueous sample analysis was performed using an immersion probe equipped with an InGaAs detector, which generated an acceptable calibration model having RMSEP of 4.25% and R p 2 of 0.94. These results show the great potential of NIR spectroscopy for achieving fast, accurate, and nondestructive analysis, and its highly adaptability for maintaining an ethanol bioconversion system.TGS, triglycine sulfate

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A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages, sex and color by means of visible and near infrared reflectance spectroscopy

Cited by 13 publications

References 25 publications

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Line monitoring by near-infrared chemometric technique for potential ethanol production from hydrothermally treated Eucalyptus globulus

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