The pH of fish muscle is an important index for quality assessment, but the traditional methods using a pH meter and probe/electrode are destructive, time-consuming, and laborious, making them unsuitable for on-line meat-quality monitoring. Hence, an approach of using fluorescence fingerprints (FFs) for the non-destructive prediction of pH in frozen fish fillets was trialled. Sixty-three live horse mackerel (Trachurus japonicus) and spotted mackerel (Scomber australasicus) were freshly harvested, sacrificed instantly, then preserved in ice, filleted, vacuum-packed, and frozen. Subsequently, the FFs of all frozen fillets were recorded using a fibreoptic-equipped fluorescence spectrometer, and the corresponding pH values of the samples were measured. After pre-processing, the masked FFs were modelled using partial least squares regression (PLSR) for the prediction of pH values. The results revealed that the developed method was accurate enough for predicting the pH changes in frozen horse mackerel and spotted mackerel fillets with R² = 0.71 and R² = 0.90, respectively. The proposed technique could be utilized as a rapid and non-contact alternative to traditional pH electrodes for the quality monitoring of fish products.

A method combining fluorescence fingerprint (FF) measurement with an imaging technique was developed to visualize the distributions of gluten and starch in dough. The fluorescence images of dough at the under-, optimum- and over-mixing stages were taken with a charged coupled device (CCD) camera in multiple excitation and emission wavelengths constructing the FF. This data containing the FF data of each pixel in the sample image was colored according to the similarity of the FF of each pixel and the FF of gluten or starch. Furthermore, the areas corresponding to air bubbles in the dough were masked in black. The resulting image, referred to as the FF pseudocolor image, showed the distributions of gluten and starch in the dough with the incorporated bubbles. Quantitative parameters concerning gluten and starch distributions and bubble area were extracted from the dough images at each mixing stage. It was shown that in the under-mixing stage, the distributions of gluten and starch were more uneven and the bubbles were smaller than those in the optimum-mixing stage. The evenness of gluten and starch distributions stayed nearly the same from the optimum- to over-mixing stages but the total bubble area increased significantly. This imaging method is suggested to have an advantage over conventional imaging methods such as light or electron microscopy, since there is no need to preprocess the sample, and any constituent in the sample can be visualized as long as there is information about the FF of the pure target constituent.

This study aimed to develop a method for the detection of food residues on the surface of stainless steel plates using fluorescence fingerprint (FF). Extracts of 20 food products were dropped on stainless steel plates as a model of food residues. Fluorescence fingerprint measurement of the food residues was carried out; the food residue samples were subsequently collected by swabbing the surface to determine adenosine triphosphate (ATP) luminescence. Partial least squares regression (PLSR) models were constructed to predict ATP luminescence (R² = 0.60; RMSE = 1.57×10⁵ RLU) and solid content (R² = 0.46; RMSE = 0.89×10⁻⁴ g) on stainless steel surfaces from the FF data. From the coefficients of the prediction model, NADH and NADPH showed the greatest contribution to the prediction of solid content.