Machine Learning Approach Using a Handheld Near-Infrared (NIR) Device to Predict the Effect of Storage Conditions on Tomato Biomarkers

Emsley, Natalia E. M.; Holden, Claire; Guo, Sarah; Bevan, Rhiann S.; Rees, Colin; McAinsh, Martin Robert; Martin, Francis; Morais, Camilo L. M.

doi:10.1021/acsfoodscitech.1c00420

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Each PC is orthogonal to the other and consists of scores and loadings. The scores represent variance in sample direction and are used to identify patterns of similarity between samples, while the loadings represent variance in the wavelength direction (Emsley et al., 2022). Thus, PC 1 (74.4%) and PC 2 (17.4%) cumulatively explained 91.8% of the variation in NIRS spectra.…”

Section: Resultsmentioning

confidence: 99%

“…Algorithmically, SVMs build optimal separating boundaries between datasets by solving a constrained quadratic optimization problem (Cristianini & Shawe‐Taylor, 2000). NIRS, coupled with SVM, have had diverse applications and have been deployed with high classification accuracies in identification of plant diseases (Mishra et al., 2012), predicting forage quality (Baath et al., 2020), distinguishing transgenic and non‐transgenic Brassica (Sohn et al., 2022), and predicting storage conditions and time after harvest for tomatoes (Emsley et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

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Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava

Kanaabi,

Namakula,

Nuwamanya

et al. 2023

The Plant Genome

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This study focuses on meeting end‐users’ demand for cassava (Manihot esculenta Crantz) varieties with low cyanogenic potential (hydrogen cyanide potential [HCN]) by using near‐infrared spectrometry (NIRS). This technology provides a fast, accurate, and reliable way to determine sample constituents with minimal sample preparation. The study aims to evaluate the effectiveness of machine learning (ML) algorithms such as logistic regression (LR), support vector machine (SVM), and partial least squares discriminant analysis (PLS‐DA) in distinguishing between low and high HCN accessions. Low HCN accessions averagely scored 1–5.9, while high HCN accessions scored 6–9 on a 1–9 categorical scale. The researchers used 1164 root samples to test different NIRS prediction models and six spectral pretreatments. The wavelengths 961, 1165, 1403–1505, 1913–1981, and 2491 nm were influential in discrimination of low and high HCN accessions. Using selected wavelengths, LR achieved 100% classification accuracy and PLS‐DA achieved 99% classification accuracy. Using the full spectrum, the best model for discriminating low and high HCN accessions was the PLS‐DA combined with standard normal variate with second derivative, which produced an accuracy of 99.6%. The SVM and LR had moderate classification accuracies of 75% and 74%, respectively. This study demonstrates that NIRS coupled with ML algorithms can be used to identify low and high HCN accessions, which can help cassava breeding programs to select for low HCN accessions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava

Kanaabi,

Namakula,

Nuwamanya

et al. 2023

The Plant Genome

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“…Emsley et al assessed the NIR potentiality to prove tomato stability during storage. Resulting informations indicated the possibility to predict tomatoes time-after-harvest ( Emsley et al, 2022 ). Arruda de Brito et al also proposed the possibility to determine important parameters (color, dry matter, etc) by NIR spectroscopy ( Arruda de Brito et al, 2022 ).…”

Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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