VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

Folch-Fortuny, Abel; Prats-Montalbán, José Manuel; Cubero, Sergio; Blasco, José; Ferrer, Alberto

doi:10.1016/j.chemolab.2016.05.005

Cited by 69 publications

(37 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this reason, being it the fastest preprocessing method (because no correction of the spectrum is necessary), it could be selected as the most appropriate in this case. This is also in accordance to previous works with hyperspectral imagery . Multiplicative scatter correction was discarded because it provided very different results depending on the reference spectrum used.…”

Section: Discussionsupporting

confidence: 81%

“…This is also in accordance to previous works with hyperspectral imagery. 42 Multiplicative scatter correction was discarded because it provided very different results depending on the reference spectrum used. Furthermore, CA allowed to specifically recognizing in a very simple and graphical way those strategies providing the higher average rates of TP, FP, FN, and TN and even selecting the model and preprocessing technique attending to a special focus on TN or TP.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Galdón-Navarro

Prats-Montalbán

Cubero

et al. 2017

Journal of Chemometrics

View full text Add to dashboard Cite

In polyethylene terephthalate's (PET)'s recycling processes, separation from polyvinyl chloride (PVC) is of prior relevance due to its toxicity, which degrades the final quality of recycled PET. Moreover, the potential presence of some polymers in mixed plastics (such as PVC in PET) is a key aspect for the use of recycled plastic in products such as medical equipment, toys, or food packaging. Many works have dealt with plastic classification by hyperspectral imaging, although only some of them have been directly focused on PET sorting and very few on its separation from PVC. These works use different classification models and preprocessing techniques and show their performance for the problem at hand. However, still, there is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method, when using NIR hyperspectral images.

show abstract

Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Galdón-Navarro

Prats-Montalbán

Cubero

et al. 2017

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…The highest classification accuracy of 82% was obtained for insect‐infested apples (Rady and others ). The multispectral imaging technique has also been widely investigated to detect various types of defects (such as insect damage, bruising, decay, cold injury, black heart, puncture injury, and cracks) on various plant foods (such as peach, radish, sunflower seed, citrus, and jujube) (Ma and others ; Zhang and others ; Folch‐Fortuny and others ; Li and others , ; Liu and others ; Pan and others ; Song and others ; Wu and others ). Based on feature wavelengths associated with corresponding defects, simplified models (such as soft independent modeling of class analogy (SIMCA), PCA, ANN, LS‐SVM, FLDA, and MNF) were conducted for nondestructively assessing defects on such plant foods with classification accuracies of over 90%.…”

Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 99%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

show abstract

“…But to analyse internal composition it is necessary the use of technology sensible to non-visible wavelengths related with chemical compounds. This can be achieved by using hyperspectral imaging (Lorente et al, 2012) that is a powerful non-invasive technology that allows obtaining the spatial distribution of the spectral information and it is being used from recent in the internal quality inspection of food (Cheng et al, 2016a;Cheng et al, 2016b, Gómez-Sanchis et al, 2013 or to assess some properties of fruits like the ripeness in apples (ElMasry et al, 2008), citrus fruits (Folch-Fortuny et al, 2016), pepper (Schmilovitch et al, 2014), or mango (Velez-Rivera et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

Munera

Besada

Aleixos

et al. 2017

LWT

View full text Add to dashboard Cite

The internal quality of intact persimmon cv. 'Rojo Brillante' was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO 2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic discriminant analysis (LDA and QDA) and support vector machine (SVM). Additionally, flesh firmness was predicted by partial least square regression (PLSR). The full spectrum was used to determine the internal properties and later principal component analysis (PCA) was used to select optimal wavelengths (580, 680 and 1050 nm). The correct classification was above 92% for the three classifiers in the case of ripeness and 95% for QDA in the case of astringency. A value of R 2 = 0.80 and a ratio of prediction deviation (RPD) of 1.86 were obtained with the selected wavelengths for the prediction of firmness which demonstrated the potential of hyperspectral imaging as 1 a non-destructive tool in the assessment of the firmness, ripeness state and astringency level of 'Rojo Brillante' persimmon.

show abstract

VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

Cited by 69 publications

References 41 publications

Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

Contact Info

Product

Resources

About