Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles

• Spectral absorption index was used to extract spectral morphological features.
• Spectra pretreated by multiplicative scatter correction were better than the raw.
• The absorption values proved to be better than the reflectance for building models.
• Moisture content was effectively predicted by the spectral morphological features.

Spectral absorption index was proposed to extract the morphological features of the spectral curves in pork meat samples (longissimus dorsi) under the conditions including fresh, frozen–thawed, heated–dehydrated and brined–dehydrated. Savitzky–Golay (SG) smoothing and multiplicative scatter correction (MSC) were used for calibrating both the spectral reflectance and absorbance values. The absorption values were better than the reflectance values and the calibrated spectra by MSC were better than the raw and SG smoothing corrected spectra in building moisture content predictive models. The optimized partial least square regression (PLSR) model attained good results with the MSC calibrated spectral absorption values based on the spectral absorption index features (R2P = 0.952, RMSEP = 1.396) and the optimal wavelengths selected by regression coefficients (R2P = 0.966, RMSEP = 0.855), respectively. The models proved spectral absorption index was promising in spectral analysis to predict moisture content in pork samples using HSI techniques for the first time.