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Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-4528-4989
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-5341-3656
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-5480-0167
2024 (English)In: Horticulturae, E-ISSN 2311-7524, Vol. 10, no 4, p. 336-336Article in journal (Refereed) Published
Abstract [en]

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

Place, publisher, year, edition, pages
2024. Vol. 10, no 4, p. 336-336
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Agricultural Science
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URN: urn:nbn:se:mdh:diva-66364DOI: 10.3390/horticulturae10040336ISI: 001210006600001Scopus ID: 2-s2.0-85191572101OAI: oai:DiVA.org:mdh-66364DiVA, id: diva2:1848634
Available from: 2024-04-04 Created: 2024-04-04 Last updated: 2024-05-08Bibliographically approved

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Gorji, ReyhanehSkvaril, JanOdlare, Monica

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