Accurate Quantification of Fungal Growth in Bread by Using Spectral Analysis

Abstract

Traditional methods for detection and enumeration of microbial growth in food stuff are very time consuming, destructive, invasive, complex, expensive and risky especially in case of pathogenic microbes. Therefore, the experimental work comprehensively detailed in this study was carried with the aim of non-destructive detection and quantification of fungal growth in bread using spectral analysis as one of the most promising techniques. For a period of seven consecutive days, spectral images in the near infrared (NIR) range were acquired for freshly-backed bread samples. Concurrently, the corresponding mould growth was monitored and assessed with the standard plating methods. Spectral data extracted from the images of bread samples and their reference mould counts during the storage period were modelled using multivariate statistical models. The principal component analysis (PCA) indicated that bread samples at the first four days consistently had similar spectral fingerprint and projected at the same location in the principal component plot. Starting from the fifth day, bread samples exhibited extraordinary spectral behaviour. Moreover, results demonstrated good prediction of mould counts in calibration and validation sets of bread samples (= 0.97 and = 0.94). The results presented in this work revealed that the biochemical fingerprints during fungal invasion conveyed by NIR spectral images in combination with the appropriate multivariate analysis strategy have significant potential for rapid assessment of bread spoilage.

Innovations in nondestructive assessment of baked products: Current trends and future prospects

“…Research indicates that HSI can be a promising tool for measuring and quantifying microbial activity in baked goods. Morsey et al (2014) used NIR HSI to detect mold growth on freshly baked bread slices after 7 days of storage. The microbial activity and population increase were monitored and assessed using conventional plate counting.…”

“…Morsey et al. (2014) used NIR HSI to detect mold growth on freshly baked bread slices after 7 days of storage. The microbial activity and population increase were monitored and assessed using conventional plate counting.…”

Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging

“…Examples include the moisture contents of baguette slices after 96 h of storage (950-2495 nm) [21], bread [22], and biscuits (950-2500 nm) [23,24]; fats and moisture content of doughnuts (1100-2100 nm) [25]; the moisture content and hardness of cakes (900-1700 nm) [26]; and the water activity prediction of mamón cakes (Filipino sponge) (935-1720 nm) [27]. Other studies have used HSI to monitor the safety and shelf-life of different baked goods, for example, mold detection in bread slices after 7 and 21 days of storage [28,29] and in cake (935-1720 nm) [30,31]. HSI has also been utilized to achieve various objectives, such as pulse flour classification based on protein and color (400-1000 nm and 1000-2500 nm) [15], segregation of bison muscles based on color stability [32], classification of pork loin based on brine concentration (450-1664 nm) [33], identification of different particle sizes of milk powders [34] (400-1000 nm), discrimination of maize kernels (874-1733 nm) [18], and hardness prediction of wheat kernels [35].…”

Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

“…The developed model performed well for white sponge cakes as compared to chocolate ones. Noha Morsy et.al., [32] predicted the microbial spoilage in bread using the NIR imaging system. The spoilage was analyzed by PCA and predicted with the help of Partial Least Squares.…”