Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods

Neto, Antônio José Steidle; Lopes, Darci Clementino; Zolnier, Sérgio; Silva, Thieres George Freire da

doi:10.1017/s0021859618000539

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Vis-NIR spectroscopy may be used in a number of applications, including the classification of sugarcane varieties, with promising results [ 42 ]. The same authors showed that the spectral regions between 650 and 750 nm, corresponding to the visible spectrum, was the most suitable for sugarcane discrimination.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Corrêdo

Maldaner

Bazame

et al. 2021

Sensors

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Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Corrêdo

Maldaner

Bazame

et al. 2021

Sensors

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“…As for the varieties discrimination of apples, both back propagation neural network (BPNN) with the preprocessing of WT (49) Sugarcane contains a large amount of sugar and is a renewable energy source of biofuel, and the classification of sugarcane varieties contributes to the sugarcane breeding program. Steidle et al (96) used the VIS/NIR spectroscopy (450-1,000 nm) to measure spectral reflectance in the center of each sugarcane stalk divided area for four sugarcane varieties discrimination. PLS-DA, FDA, and SFDA using full spectra obtained the classification accuracy of 82, 81, and 74%, respectively.…”

Section: Beveragementioning

confidence: 99%

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Liu

Zhu

et al. 2021

Front. Nutr.

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Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

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“…This technique has already been successfully used to separate batches of species in nurseries, discriminate nonvisually differentiable cultivars, and identify native tree species (Borraz-Martínez et al 2019; Neto et al 2018; Soares et al 2017; Wang and Yu 2015). In addition, the high speed of acquisition of NIR spectra, in combination with multivariate calibration techniques, has enabled the online classification (in real time) of fruits and vegetables based on quality attributes; on the other hand, when considering only the appearance of fruit, this technique has not adequately demonstrated the ability to detect differences in quality (Cunha Júnior et al 2016; Souza et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Braga

Chiconi²,

Bacha³

et al. 2023

Weed Sci

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The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which, in the future, might be coupled to machines to optimize the performance of a species-specific weed management. Thus, the objective of this study was to use near infrared spectroscopy (NIR) and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of three weed species in vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm-1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden’s index and analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of pre-processing was performed. It was possible to obtain an accuracy of 99.3, 98.5, and 98.7% for ivylead morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth] and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA), or associated with partial least squares regression with discriminant analysis (PLS-DA), can be used to discriminate morningglory species.

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Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods

Cited by 11 publications

References 37 publications

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

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