Nondestructive Hygiene Monitoring on Pork Meat Surface Using Excitation–Emission Matrices with Two-Dimensional Savitzky–Golay Second-Order Differentiation

Investigating spectroscopic and copper binding characteristics of dissolved organic matter in wastewater using EEMs with two-dimensional Savitzky-Golay second-order differentiation-PARAFAC

Dissolved organic matter (DOM) in wastewater interacts with heavy metal particles in aquatic environments, which changes their dynamics and bioavailability. For quantifying the DOM, an excitation-emission matrix (EEM) paired alongside parallel factor analysis (PARAFAC) is typically employed. However, a drawback of PARAFAC has been revealed in recent studies, i.e., the rise of overlapping spectra or wavelength shifts in fluorescent components. Here, traditional EEM-PARAFAC and, for the first time, two-dimensional Savitzky-Golay second-order differential-PARAFAC (2D-SG-2nd-df-PARAFAC) were used to study the DOM-heavy metal binding. The samples from four treatment units of a wastewater treatment plant, i.e., influent, anaerobic, aerobic, and effluent, underwent the process of fluorescence titration with Cu2+. Four components were separated with dominant peaks in regions I, II, and III (proteins and fulvic acid-like) through PARAFAC and 2D-SG-2nd-df-PARAFAC. A single peak was observed in region V (humic acid-like) by PARAFAC. In addition, Cu2+-DOM complexation indicated clear differences in DOM compositions. The binding strength increased between Cu2+ and fulvic acid-like components in contrast to protein-like components from influent to the effluent, and increasing fluorescence intensity with the addition of Cu2+ in the effluent indicated changes in their structural composition. Moreover, when comparing both methods, the 2D-SG-2nd-df-PARAFAC provided the components without peak shifts and better fitting for Cu2+-DOM complexation model, demonstrating it to be a more reliable technique compared to only traditional PARAFAC for DOM characterization and quantifying metal-DOM in wastewater.

Characterizing the interactions between copper ions and dissolved organic matter using fluorescence excitation-emission matrices with two-dimensional Savitzky-Golay second-order differentiation

Monitoring dissolved organic matter (DOM) content in aquatic environments is crucial for not only understanding the dynamics of heavy metals but also predicting their bioavailability. Fluorescence spectroscopy is typically employed to characterise DOM. Here, the interaction between DOM and trace metals was investigated by combining excitation-emission matrix (EEM) quenching with two-dimensional Savitzky-Golay second-order differentiation (2D-SG-2nd-df) analysis. The 2D-SG-2nd-df analysis decomposed the EEM spectra of commercial humic acid (HA) samples into six separate fluorescence peaks, which agreed with the results obtained through conventional parallel factor (PARAFAC) analysis. Compared with PARAFAC modeling, the 2D-SG-2nd-df approach provided more valid and reliable results when the dataset contained distinct samples. Moreover, since the results obtained from 2D-SG-2nd-df for each sample are independent, shifts in the peak wavelength can be reproduced more efficiently using this method. Triplicate titration experiments showed clear differences in HA-copper interactions for samples with different HA composition and molecular weight. The binding strength between copper and low-molecular-weight DOM in water was weaker than that observed for high-molecular-weight DOM. The results obtained in this study will serve as a basis for applying 2D-SG-2nd-df not only to DOM but also to other samples studied using EEM measurements.

Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique

Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.