Methodological approach for calibration transfer from benchtop to portable SWIR spectrometers: a laboratory-scale study on powdered grapevine leaves

Near- and short-wave infrared spectroscopy has become increasingly relevant for the assessment of plant composition, yet the reproducibility of chemometric models across different instruments remains a major limitation. This work proposes a methodological approach for calibration transfer from a benchtop hyperspectral imaging (HSI) system to a portable SWIR spectrometer, using powdered grapevine leaves as a controlled case study. The strategy relies on Direct Standardization (DS), a technique that transforms spectra acquired under different conditions into a common space. By applying DS to a representative subset of samples, models trained under laboratory conditions can be adapted to spectra acquired with portable devices. Two complementary modelling strategies were adopted: an Error-Correcting Output Codes Support Vector Machine (ECOC-SVM) classifier, used to assess qualitative improvements in class discrimination before and after DS, and an eXtreme Gradient Boosting (XGB) regression model, developed for quantitative prediction of elemental concentrations measured by micro-XRF. Validation on an independent set of homogenized leaf powders confirmed that DS markedly reduced inter-instrument spectral divergence, improving class separability and enabling accurate regression of macro- and micronutrients (Fe, Mn, P, S, Zn, Ca, K, Si). Although limited to laboratory-scale samples, the study demonstrates that calibration transfer is effective in harmonizing spectral domains. The proposed workflow provides a reproducible and scalable methodology for cross-instrument adaptation, with potential applicability to diverse agricultural products and portable spectroscopy platforms.