Nondestructive freshness evaluation of mackerel fish using Vis/NIR hyperspectral imaging and multivariate analysis

Nondestructive freshness evaluation models for chub mackerel (Scomber japonicus) fillets were developed using visible/near-infrared (Vis/NIR) hyperspectral imaging and multivariate regression analysis. Total 96 mackerel samples were investigated during 6 days of storage under five different conditions for measurement of pH, total volatile basic nitrogen (TVB-N), and K values along with acquisition of hyperspectral images. With partial least squares regression (PLSR) and support vector regression (SVR) along with wavelength selection method using Variables Importance in Projection (VIP) scores, performances of PLSR, VIP-PLSR, SVR, and VIP-SVR models were evaluated and compared. The VIP-PLSR models showed the best performance for predicting the freshness indicators, with R2 values of 0.86, 0.86, and 0.91 for pH, TVB-N, and K values, respectively. Furthermore, it was shown that the identification and removal of noise pixels from the hyperspectral data based on correlation analysis was effective in improving the regression results. •Vis/NIR hyperspectral images were used to evaluate the freshness of mackerel fillets.•Four types of multivariate regression models were developed and compared.•Significant wavelengths were identified for each freshness indicator using VIP scores.•VIP-PLSR models had the best performances on predicting the pH, TVB-N, and K values.•Identification and removal of less relevant spectra enhanced the model performances.