Non-destructive and rapid measurement of sugar content in growing cane stalks for breeding programmes using visible-near infrared spectroscopy

Phuphaphud, Arthit; Saengprachatanarug, Khwantri; Posom, Jetsada; Maraphum, Kanvisit; Taira, Eizo

doi:10.1016/j.biosystemseng.2020.06.012

Cited by 20 publications

(9 citation statements)

References 30 publications

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“…These results indicated that the sample set composed for many different varieties did not affect the spectral characteristics between each variety for both sample types. A similar effect was observed by Phuphaphud et al (2020) [ 14 ] when evaluating the classification of three varieties. Therefore, the present study was conducted for all varieties combined.…”

Section: Resultssupporting

confidence: 84%

“…Therefore, this spectral region was removed from the data set. Phuphaphud [ 14 ] observed the same effect due to the skin color of sugarcane billets. Also, based on the evaluation of the coefficient of variation (CV) obtained for each spectral band, the last spectral bands showed high CV concerning their neighbors and were also removed from the dataset, similar to the procedure performed by Franceschini [ 27 ] in a study on the external effects on the spectral reading of vis-NIR of soil samples using the same equipment.…”

Section: Resultsmentioning

confidence: 76%

“…Fibre content has no relation with some important attributes, such as Brix and Pol, and only minimally impacts TRS calculation. The prediction of this parameter is important for producing energy cane and breeding programs, as in work developed by Phuphaphud et al (2019) [ 14 ].…”

Section: Resultsmentioning

confidence: 99%

“…This basic principle has allowed substantial scientific advances to predict organic compounds of agricultural products associated with its quality using vis-NIR spectroscopy as a nondestructive and environmentally friendly analysis technique [ 5 ]. Moreover, several studies have shown promising results when using the technique to predict sugar cane quality in the sugar mill [ 9 , 10 , 11 , 12 ] and for breeding programs [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Resultssupporting

confidence: 84%

Section: Resultsmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Each treatment combination was measured 10 times to obtain data on the working push and pull forces. Duncan's different reach test (DMRT) was picked as a technique for making various examinations with a critical distinction at 5% [18]- [20]. The ANOVA and DMRT were finished by utilizing Microsoft office Excel.…”

Section: Figure 2 Experimental Set-upmentioning

confidence: 99%

Measurement push and pull forces on automatic liquid dispensers

Sitorus

Pramono

Siregar

et al. 2021

IJECE

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Since the COVID-19 pandemic, automated liquid dispensers have been increasingly developed to assist transmission prevention. However, data availability of automatic liquid dispenser mechanism's technical characteristics is not yet widely available. This causes frequent over or under design in its development. Therefore, we specifically measure push and pull forces engineering characteristics generated by the automatic liquid dispenser mechanism. A wire mechanism-based automatic liquid dispenser apparatus was used to experiment. A load-cell sensor was used to detect the force that occurs from a servo motor controlled by a microcontroller. The force data (push and pull) will be sent directly to the database server cloud with a recording frequency of every second. Three types of fluid treatment levels are used i.e. water, liquid soap, and hand sanitizer gel. Three types of fluid volume treatment levels used were 50 ml, 150 ml, and 250 ml. Each treatment level combination is carried out at the servo motors rotation steps 180°, 150°, 120°, 90°, 60°, and 30°. The results show that no significant differences were found in maximal forces required to release the water, liquid soap, and hand-sanitizer gel. It is also known that the volume of the fluid has a very significant effect on the amount of push and pull forces generated.

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Non-destructive quantitative analysis of carotene content in carrots using Raman spectroscopy

Wang

Zhang

Hong

et al. 2021

Eur Food Res Technol

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Non-destructive and rapid measurement of sugar content in growing cane stalks for breeding programmes using visible-near infrared spectroscopy

Cited by 20 publications

References 30 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

Measurement push and pull forces on automatic liquid dispensers

Non-destructive quantitative analysis of carotene content in carrots using Raman spectroscopy

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