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Microorganism biofilms can form on food-contact surfaces and lead to cross-contamination of bacteria during food processing. The dynamics of biofilm formation by Pseudomonas fluorescens associated with meat spoilage and the resistance of established biofilm to four disinfectants were investigated in this study. The results indicated that more than 4.5 log CFU/cm 2 of P. fluorescens were transferred to a stainless-steel surface under short-term (5 h) scenario, and bacteria transfer occurred even after only 10 min exposure. A mature biofilm was discovered after 5 days of incubation under long-term (7 days) scenario. The biofilm was characterized by more than 9.5 log CFU/cm 2 cells, 120 μm thick and a large amount of extracellular polymeric substances. Observation of mature biofilm were further confirmed by confocal laser scanning microscopy and attenuated total reflection fourier-transformed infrared spectroscopy. The established biofilm showed distinct resistance to four disinfectants, namely sodium hypochlorite (SH), chlorine dioxide (CD), slightly acidic electrolyzed water (sAEW) and acidic electrolyzed water (AEW). The cells reduction was significant dependent on the concentrations, almost 8.0 log CFU/cm 2 of biofilm cells were reduced by 40 mg/L AEW and 200 mg/L SH treatment for 30 min, below the detection limit (1.32 log CFU/cm 2 ). However, less than 3 log CFU/cm 2 reduction of biofilm cells were obtained after treatment with 40 mg/L sAEW and 20 mg/L CD for 30 min, remaining large numbers of biofilm survival. Meanwhile, the cells reduction was time-dependent, and the numbers of surviving cells rapid declined along with increasing time, but there was no additional log reduction when treatment of 20 mg/L CD was increased from 15 to 30 min. The finding suggested that AEW has great potential to be used as an environmentally friendly intervention alternative for the elimination of P. fluorescens biofilm, substituting for the traditional sodium hypochlorite in the food industry. [ABSTRACT FROM AUTHOR]

Butyric acid (butyrate) is a candidate marker of milk fat in complex fat blends, since it is exclusive of milk triacylglycerols (TAGs) from different ruminant species. In this work, we determined the amount of milk fat used for the preparation of fat blends by 13 C Nuclear Magnetic Resonance ( 13 C NMR) spectroscopy-based quantification of butyrate. When tested on fat samples spiked with known amounts of reference bovine milk fat (BCR-519 certified material), the relative composition of the mixtures was reliably assessed through the integration of the diagnostic 13 C NMR carbonyl (C1) or α-carbonyl methylene (C2) resonances of butyrate. NMR data exhibited strict correlation with high resolution-gas chromatography (GC) of fatty acid methyl esters (R 2  = 0.99), which was used as an independent and well-established method for the determination of butyrate. Thus, 13 C NMR can be used for the direct assessment of milk fat content in fat mixtures, at a limit of detection lower than 5%, with clear advantages over the traditional GC methods in terms of speed, robustness and minimal sample handling. The natural variability of butyrate in milk has been taken into account to estimate the uncertainty associated with the milk fat content in unknown fat blends. [ABSTRACT FROM AUTHOR]

In this study, proton-transfer reaction mass spectrometry (PTR-MS) and near-infrared spectroscopy (NIRS) were compared for the authentication of geographical and farming system origins of Brazilian coffees. For this purpose, n = 19 organic (ORG) and n = 26 conventional (CONV) coffees from distinct producing regions were analyzed. Overall, differences (p ≤ 0.05) in 44 and 71 ion intensities were observed between the main producing regions and farming systems, respectively. Principal component analysis was not effective in illustrating differences between the coffees according to the farming system or geographical origin using neither PTR-MS nor NIRS data. However, when the PLS-DA was applied, which produced the best performing models compared to several other chemometric techniques, the farming system was adroitly differentiated. The fact that the classification performance (>80%) was independent of the data acquisition method used gives NIRS an edge over PTR-MS in the differentiation of the farming system because of its rapid analysis and cost. Differentiating geographic location of coffee was rather complex. The PTR-MS calibration models showed slightly better PLS-DA classification rates compared to the NIRS models (69% vs. 61%, respectively), which is even more evident when the alternative classifier is used (LDA-kNN, 69% vs. 39%, respectively). Coffee samples from either Minas Gerais (MG) or Sao Paulo (SP) were differentiated from the other regions. In conclusion, our study provides information on alternative rapid analysis coupled with chemometric techniques to differentiate the farming system and trace the geographical provenance of Brazilian specialty coffee. [ABSTRACT FROM AUTHOR]

Growing trends towards the development of rapid, simple, and cost-effective methodologies for grain quality monitoring have been observed for a long time. Infrared spectroscopy associated with chemometrics has proven to be a promising alternative to traditional wet chemistry methods. So far comparative research that focuses on the relation between spectral preprocessing, wavelength selection, and model performance is rare. This paper aims to explore the effect of spectral preprocessing and wavelengths selection on the performance of partial least square (PLS) regression models constructed with near-infrared (NIR) and mid-infrared (ATR-FT/MIR) spectroscopy. Eighty wheat samples were randomly selected in Western Canada. NIR (680–2500 nm) and MIR (4000–700 cm −1 ) spectra were collected and calibrated with reference crude protein (CP) and moisture content using PLS regression technique based on different spectral preprocessing methods and selected important wavelengths. In a general way, the models generated with NIR spectra showed superior performance than that developed with MIR spectra. For CP, the best NIR model was developed with the 1400–2500 nm spectral range of standard normal variate (SNV) and first derivative (FD) pretreated spectra, which showed an excellent prediction performance (R 2 = 0.97); the best MIR model was established using the 1750–1100 cm −1 region of SNV corrected spectra, which gave a good predictive ability (R 2 = 0.90). Regarding to the moisture, the best model obtained by NIR technique (based on the 1100–2500 nm region of raw spectra) showed a good prediction performance (R 2 = 0.86), while the best model generated by MIR technique (using the full wavenumber range of FD-SNV preprocessed spectra) only gave approximate quantitative prediction (R 2 = 0.72). Both spectral preprocessing and wavelength selection could affect the performance of PLS models for predicting CP and moisture content in wheat. More effort is needed to improve the performance of ATR-FT/MIR spectroscopy for quantitative analysis based on appropriate chemometrics tools. [ABSTRACT FROM AUTHOR]

Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. Nowadays, EBNs in market are graded by some conventional features presented by EBN products, such as material purity, geographical origin, production environment or color. Since different EBNs have various prices, two common types of fraud with EBN, namely substituting the EBN ingredient with cheap non-EBN materials and mislabeling of a country of origin or production environment, have occurred for a long time. To explore a feasible method for identifying EBNs, Fourier Transform Infrared Spectroscopy (FTIR) system and sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) combined with multivariate analysis were employed. The results showed that FTIR could discriminate between authentic and fake EBNs since there were big differences in spectra of these samples. On the other hand, protein band data obtained by SDS-PAGE could be used for distinctly identifying geographical origin of house nests and the production environment of EBNs. It is concluded that FTIR in combination with SDS-PAGE is a promising method for comprehensive determination of EBNs. [ABSTRACT FROM AUTHOR]

Quantifying mold growth is of great relevance and interest in microbiology. However, predictive modeling of filamentous fungal growth has been hampered by the lack of an appropriate and accurate method for quantification. An adequate, rapid and objective method will allow studying the effect of many different parameters and conditions on mold growth patterns and can thus provide valuable insight and knowledge. This study outlines a new approach for quantifying mold growth by providing an accurate tool for measuring different segments of mold colonies. The method is based on clustering multispectral images by k -means, an unsupervised and simple clustering algorithm. In order to demonstrate the efficiency of the new approach, three different sample sets were analyzed by the developed method, with the objective of quantifying mold growth and size of the colony segments of Penicillium mold. The results verify the ability of the proposed method to quantify mold growth and colony composition (relative size of the white and green segments) accurately. This provides a robust measure for interpreting inhibition activity against mold in different samples and makes a quantitative comparison possible. Among the virtues of the method are: 1) the ability to quantify very small differences in the size of colonies which cannot be easily discriminated by visual inspection, 2) the ability to quantify mold growth on transparent as well as on opaque media (e.g. milk), and 3) no prior assumptions for the shape and multiplicity of colonies. The accuracy and non-destructive characteristic of the method allow dynamic quantification of mold growth which can be very valuable in predictive microbiology and in studies related to biopreservation of food products. [ABSTRACT FROM AUTHOR]

This work proposes classification models for the prediction of olive maturity index based on Fourier Transform-Near Infrared (FT-NIR) spectra of intact drupes. An image analysis (IA) method was purposely developed for the objective evaluation of the maturity index. Thirteen cultivars at different ripening stages were harvested along three years. The reliability of the IA method was confirmed by the highly significant correlation with the common visual evaluation of maturity index. Classification models were developed with Partial Least Square-Discriminant Analysis (PLS-DA), using IA results and FT-NIR spectra of olives collected in diffuse reflectance. Most PLS-DA models calculated separately for olive origin gave sensitivity and specificity values in prediction higher than 81%. The global model performed slightly worse (sensitivity, 79%; specificity, 75%), but it is definitely more robust and can provide the olive sector with a fast, green and non-destructive olive sorting method for the production of high quality virgin oil. • Large olive dataset based on different origins, cv, ripening stages and crop years. • Development of an image analysis method for objective ripening class identification. • Image analysis classes used as references for PLS-DA models based on FT-NIR data. • Good global classification with high sensitivity (79%) and specificity (75%). [ABSTRACT FROM AUTHOR]

Goat milk has a potential target of fraud. In this sense, Near Infrared Spectroscopy (NIRS) have been successfully used to detect food fraud. This study aimed to develop multivariate classification models using NIRS to detect adulterants in goat milk. Principal Component Analysis (PCA), control chart, k-Nearest Neighbor (k-NN), Part Least Square-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogies (SIMCA) were used to detect the adulterants: water, urea, bovine whey and cow's milk in goat's milk samples with concentrations of 0 (control), 1, 5, 10, 15 and 20% v/v, resulting in 300 control samples and 300 adulterated samples. The control chart discriminated authentic and adulterated samples with 95% confidence. The PLS-DA results were better compared to those obtained by k-NN and SIMCA; presenting 100% sensitivity and specificity in calibration, cross validation, and prediction. Therefore, NIRS combined with PLS-DA was adequate to detect goat milk safety control associated with adulteration. • PLS-DA models presented 100% of sensitivity and specificity. • PLS-DA models presented better results than KNN and SIMCA. • Q-control chart was capable to detect goat's milk adulteration with 95% of IC. [ABSTRACT FROM AUTHOR]