Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: A review

Saeys, Wouter; Trong, Nghia Nguyen Do; Beers, Robbe Van; Nicolaı̈, Bart

doi:10.1016/j.postharvbio.2019.110981

Cited by 118 publications

(140 citation statements)

References 108 publications

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“…Unfortunately, the data are not as clean as expected when sampling and instrument noise and typing mistakes, among others have a greater impact where the use or pre-processing or any other correction does not improve the accuracy of the analytical results (e.g., inaccuracies can never be modelled) [35][36][37][38][39]. Therefore, a word of caution: MVA is not a "black box" or "push button" approach where the modelling will automatically do the rest [35][36][37][38][39].…”

Section: Analysing and Interpreting The Information-the Mathsmentioning

confidence: 99%

“…Different authors have highlighted that one of the most important issues to be considered during sampling is related to how good the uncertainty depending on the purpose is [ 35 , 36 , 37 , 38 ]. One important issue to consider (and remember) is that the uncertainty of the measurement that arose from sampling is non-negligible [ 35 , 36 , 37 , 38 , 39 ]. This is even more significant when raw materials (e.g., food ingredients) and environmental samples (e.g., soil and water) are collected, where the uncertainty of the sampling exceeds the analytical contribution [ 35 , 36 , 37 , 38 , 39 ].…”

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

“…One important issue to consider (and remember) is that the uncertainty of the measurement that arose from sampling is non-negligible [ 35 , 36 , 37 , 38 , 39 ]. This is even more significant when raw materials (e.g., food ingredients) and environmental samples (e.g., soil and water) are collected, where the uncertainty of the sampling exceeds the analytical contribution [ 35 , 36 , 37 , 38 , 39 ]. Therefore, the theory of sampling becomes highly relevant during the development of a given applications.…”

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

“…The theory of sampling (TOS) documents and details in a comprehensive means all aspects of the mechanical structure and chemical variation within a target in relation to the procedure for obtaining a primary sample from it [ 35 , 36 , 37 , 38 , 39 ]. Some of the main issues considered in the TOS are associated with the characteristics and/or properties of the target, including the size range of the particles comprising the target, the shapes of the particles, the compositional variation of the particles and the degree and style of the heterogeneity of the target, among others [ 35 , 36 , 37 , 38 , 39 ]. The method of collecting or extracting the sample and the degree of comminution/homogenisation/grinding at the different steps during the sampling process are important aspects included in the TOS [ 35 , 36 , 37 , 38 , 39 ].…”

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

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The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

Cozzolino

2020

Molecules

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The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars—the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)—that support the development and implementation of vibrational spectroscopy applications.

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Section: Analysing and Interpreting The Information-the Mathsmentioning

confidence: 99%

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

Section: The Source Of Information—the Experiments and The Samplementioning

confidence: 99%

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The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

Cozzolino

2020

Molecules

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“…Dubey and Jalal [ 20 ] reported on an improved sum and difference histogram texture feature to identify the species and variety of fruit and vegetables based on HSV (Hue, Saturation, Value) images. Abundant literatures have demonstrated appropriate algorithms that could further improve the results [ 21 , 22 , 23 , 24 ]. Ronald and Evans [ 25 ] classified apple varieties using Naive Bayes algorithms, which could obtain higher accuracies than those of principal components analysis (PCA), fuzzy logic, and MLP-Neural.…”

Section: Introductionmentioning

confidence: 99%

Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System

Huang

Yang

Sun

et al. 2020

Sensors

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This paper reports the nondestructive detection of apple varieties using a multichannel hyperspectral imaging system consisting of an illumination fiber and 30 detection fibers arranged at source–detector distances of 1.5–36 mm over the spectral range of 550–1650 nm. Spatially resolved (SR) spectra were obtained for 1500 apples, 500 each of three varieties from the same orchard to avoid environmental and geographical influences. Partial least squares discriminant analysis (PLSDA) models were developed for single SR spectra and spectral combinations to compare their performance of variety detection. To evaluate the effect of spectral range on variety detection, three types of spectra (i.e., visible region: 550–780 nm, near-infrared region: 780–1650 nm, full region: 550–1650 nm) were analyzed and compared. The results showed that the single SR spectra presented a different accuracy for apple variety classification, and the optimal SR spectra varied with spectral types. Spectral combinations had better accuracies for variety detection with best overall classifications of 99.4% for both spectral ranges in the NIR and full regions; however, the spectral combination could not improve the results over the optimal single SR spectra in the visible region. Moreover, the recognition of golden delicious (GD) was better than those of the other two varieties, with the best classification accuracy of 100% for three types of spectra. Overall, the multichannel hyperspectral imaging system provides more spatial-spectral information for the apples, and the results demonstrate that the technique gave excellent classifications, which suggests that the multichannel hyperspectral imaging system has potential for apple variety detection.

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Optical Sensing Technologies for Nondestructive Quality Assessment in Dry Beans

Mendoza¹,

Wiesinger²,

Cichy³

2021

Dry Beans and Pulses

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Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: A review

Cited by 118 publications

References 108 publications

The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System

Optical Sensing Technologies for Nondestructive Quality Assessment in Dry Beans

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