Colorimetric Sensor Array for Monitoring, Modelling and Comparing Spoilage Processes of Different Meat and Fish Foods

Abstract

Meat spoilage is a very complex combination of processes related to bacterial activities. Numerous efforts are underway to develop automated techniques for monitoring this process. We selected a panel of pH indicators and a colourimetric dye, selective for thiols. Embedding these dyes into an anion exchange cellulose sheets, i.e., the commercial paper sheet known as “Colour Catcher®” commonly used in the washing machine to prevent colour run problems, we obtained an array made of six coloured spots (here named Dye name-CC@). The array, placed over the tray containing a sample of meat or fish (not enriched at any extend with spoilage products), progressively shows a colour change in the six spots. Photos of the array were acquired as a function of time, RGB indices were used to follow the spoilage, Principal Component Analysis to model the data set. We demonstrate that the array allows for the monitoring the overall spoilage process of chicken, beef, pork and fish, obtaining different models that mimic the degradation pathway. The spoilage processes for each kind of food, followed by the array colour evolution, were eventually compared using three-way PCA, which clearly shows same degradation pattern of protein foods, altered only according to the different substrates.

Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis‐NIR spectroscopy

“…To visualize differences between different processing steps and varieties, principal component analysis (PCA) was applied. PCA is one of the most widely used classification procedures to extract the overall characteristics of spectral data, which has robust data compression capability (Magnaghi et al., 2020). PCA is a linear transformation that transforms original variables into a new set of variables, which effectively reduces the dimension while maintaining most of the characteristics of the original data.…”

Disposable and Low-Cost Colorimetric Sensors for Environmental Analysis

“…In summary, PCA has been recognized as an effective tool for data treatment of the results obtained by colorimetric sensor devices, as evidenced by the numerous papers on this subject [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ].…”

“…Hierarchical cluster analysis (HCA) is a multivariate unsupervised analysis technique sometimes coupled to PCA for data treatment in colorimetric arrays [ 45 , 48 , 49 , 51 , 53 , 54 ]. Additionally known as hierarchical clustering, HCA aims to group subjects with similar characteristics into clusters.…”

Role of Biogenic Amines in Protein Foods Sensing: Myths and Evidence

“…In recent decades, the interest in systems able to detect food degradation, giving a quick response, simple to be read, and suitable for implementation in control systems and smart labels has increased continuously. [1][2][3][4][5][6] This interest is more valid in foods such as meat and fish. Despite the spread of vegan and vegetarian diets, their consumption has been steadily increasing worldwide for decades, also caused by the dramatic economic development of Eastern Country of the last forty years.…”