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<p>Conventional laboratory testing of moisture content and water activity in honey is often time-consuming and costly. Therefore, alternative analytical methods that are non-destructive, rapid, efficient, and cost-effective are needed, particularly for field applications. This study aims to develop predictive models for determining moisture content and water activity in liquid honey (forest and acacia) and powdered honey using portable near-infrared (NIR) spectroscopy combined with multiple linear regression (MLR) analysis Spectral data were acquired using a NIR SpectraPod (850–1700&#xa0;nm), followed by laboratory measurements for calibration. Data pre-processing with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) improved model accuracy. A simple K-nearest neighbors (KNN) analysis was included only as a preliminary check of honey-type separability. The developed models demonstrated strong predictive performance across all honey types. For individual honey varieties, the coefficient of determination (<i>R</i><sup>2</sup>) ranged from 0.75 to 0.96, while the root mean square error (RMSE) values were between 0.02 and 0.05 for water activity and 2.1–4.6% for moisture content. When acacia and forest honey data were combined, the models maintained good performance, with <i>R</i><sup>2</sup> values of 0.58–0.78 and RMSE values of 0.03–0.05 for water activity and 4.0–6.7% for moisture content. These findings demonstrate that portable NIR spectroscopy, integrated with regression modelling, is a reliable and efficient tool for rapid honey quality assessment, offering significant advantages over conventional laboratory methods.</p>

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Prediction of Moisture Content and Water Activity in Liquid and Powdered Honey Based on Portable Near-Infrared Spectroscopy and Model Machine Learning

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Ulfatu Khoiriyah,
  • Ni’matul Izza,
  • Rini Yulianingsih,
  • Anang Lastriyanto,
  • Harki Himawan,
  • Dimas Firmanda Al Riza

摘要

Conventional laboratory testing of moisture content and water activity in honey is often time-consuming and costly. Therefore, alternative analytical methods that are non-destructive, rapid, efficient, and cost-effective are needed, particularly for field applications. This study aims to develop predictive models for determining moisture content and water activity in liquid honey (forest and acacia) and powdered honey using portable near-infrared (NIR) spectroscopy combined with multiple linear regression (MLR) analysis Spectral data were acquired using a NIR SpectraPod (850–1700 nm), followed by laboratory measurements for calibration. Data pre-processing with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) improved model accuracy. A simple K-nearest neighbors (KNN) analysis was included only as a preliminary check of honey-type separability. The developed models demonstrated strong predictive performance across all honey types. For individual honey varieties, the coefficient of determination (R2) ranged from 0.75 to 0.96, while the root mean square error (RMSE) values were between 0.02 and 0.05 for water activity and 2.1–4.6% for moisture content. When acacia and forest honey data were combined, the models maintained good performance, with R2 values of 0.58–0.78 and RMSE values of 0.03–0.05 for water activity and 4.0–6.7% for moisture content. These findings demonstrate that portable NIR spectroscopy, integrated with regression modelling, is a reliable and efficient tool for rapid honey quality assessment, offering significant advantages over conventional laboratory methods.