Jan Skvaril

Senior lecturer

Mälardalen University
Country flagSweden

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About

Jan Skvaril is a Senior Lecturer at Malardalen University in Sweden. His research areas encompass various aspects of environmental science and technology, with recent articles focusing on nutrient dynamics in agriculture, optical sensing in indoor farming, wastewater quality measurement technologies, and carbon capture methods in biomass energy systems. Additionally, he explores innovative educational practices in the context of bioeconomy and e-learning for professionals.

Articles (9)

Year:

2025

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.

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2024

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2024

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2024

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2024

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2023

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2023

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2021

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2021

Collaborators (8)

Fowzia Ibrahim

King’s College London

UNITED KINGDOM
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Maria Elstad

King’s College London

UNITED KINGDOM
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Erik Omling

Lund University

SWEDEN
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GianBattista Parigi

Professore Associato Chirurgia Pediatrica - Rector's Delegate for International Cooperation

Università degli Studi di Pavia

ITALY
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Matthieu Peycelon

Associate Professor

-

FRANCE
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Monica Odlare

Mälardalen University

SWEDEN
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Xiaodan Shi

Assistant Professor

Stockholm University

SWEDEN
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Krystian Toczewski

Wroclaw Medical University

POLAND
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