Improved fed-batch processes with Wickerhamomyces anomalus WC 1501 for the production of D-arabitol from pure glycerol.

BACKGROUND: D-Arabitol, a five-carbon sugar alcohol, represents a main target of microbial biorefineries aiming to valorize cheap substrates. The yeast Wickerhamomyces anomalus WC 1501 is known to produce arabitol in a glycerol-based nitrogen-limited medium and preliminary fed-batch processes with this yeast were reported to yield 18.0 g/L arabitol. RESULTS: Fed-batch fermentations with W. anomalus WC 1501 were optimized using central composite design (CCD). Dissolved oxygen had not a significant effect, while optimum values were found for glycerol concentration (114.5 g/L), pH (5.9), and temperature (32.5 °C), yielding 29 g/L D-arabitol in 160 h, a conversion yield of 0.25 g of arabitol per g of consumed glycerol, and a volumetric productivity of 0.18 g/L/h. CCD optimal conditions were the basis for further improvement, consisting in increasing the cellular density (3✕), applying a constant feeding of glycerol, and increasing temperature during production. The best performing fed-batch fermentations achieved 265 g/L D-arabitol after 325 h, a conversion yield of 0.74 g/g, and a volumetric productivity of 0.82 g/L/h. CONCLUSION: W. anomalus WC 1501 confirmed as an excellent producer of D-arabitol, exhibiting a remarkable capability of transforming pure glycerol. The study reports among the highest values ever reported for microbial transformation of glycerol into D-arabitol, in terms of arabitol titer, conversion yield, and productivity.

Simultaneous Detection of Glucose and Fructose in Synthetic Musts by Multivariate Analysis of Silica-Based Amperometric Sensor Signals.

Silica-based electrodes which permanently include a graphite/Au nanoparticles composite were tested for non-enzymatic detection of glucose and fructose. The composite material showed an effective electrocatalytic activity, to achieve the oxidation of the two analytes at quite low potential values and with good linearity. Reduced surface passivation was observed even in presence of organic species normally constituting real samples. Electrochemical responses were systematically recorded in cyclic voltammetry and differential pulse voltammetry by analysing 99 solutions containing glucose and fructose at different concentration values. The analysed samples consisted both in glucose and fructose aqueous solutions at pH 12 and in solutions of synthetic musts of red grapes, to test the feasibility of the approach in a real frame. Multivariate exploratory analyses of the electrochemical signals were performed using the Principal Component Analysis (PCA). This gave evidence of the effectiveness of the chemometric approach to study the electrochemical sensor responses. Thanks to PCA, it was possible to highlight the different contributions of glucose and fructose to the voltammetric signal, allowing their selective determination.

Data fusion of electronic eye and electronic tongue signals to monitor grape ripening.

Two separate artificial sensors, an electronic eye (EE) and an electronic tongue (ET), were recently developed to monitor grape ripening based on the analysis of must. The aim of this research is to exploit the complementary information obtained by means of EE and ET sensing systems using different data fusion strategies, in order to develop an integrated device able to quickly and easily quantify the physico-chemical parameters that are used to assess phenolic ripeness. To this purpose, both low-level and mid-level data fusion approaches were investigated. Partial Least Squares (PLS) regression was applied to the fused data, with the aim of relating the information brought by the two sensors with twelve physico-chemical parameters measured on the must samples by standard analytical methods. The results achieved with mid-level data fusion outperformed those obtained using EE and ET separately, and highlighted that both the artificial sensors have made a significant contribution to the prediction of each one of the considered physico-chemical parameters.

Microbiota of sliced cooked ham packaged in modified atmosphere throughout the shelf life: Microbiota of sliced cooked ham in MAP.

Fourteen lots of cooked ham in modified atmosphere packaging (CH) were analyzed within a few days from packaging (S) and at the end of the shelf-life (E), after storage at 7 °C to simulate thermal abuse. Five more lots, rejected from the market because spoiled (R), were included in the study. Quality of the products was generally compromised during the shelf life, with only 4 lots remaining unaltered. Analysis of 16S rRNA gene amplicons resulted in 801 OTUs. S samples presented a higher diversity than E and R ones. At the beginning of the shelf life, Proteobacteria and Firmicutes dominated the microbiota, with Acinetobacter, Brochothrix, Carnobacterium, Lactobacillus, Prevotella, Pseudomonas, Psychrobacter, Weissella, Vibrio rumoiensis occurring frequently and/or abundantly. E and R samples were dominated by Firmicutes mostly ascribed to Lactobacillales. It is noteworthy the appearance of abundant Leuconostoc, negligible in S samples, in some E and R samples, while in other LAB were outnumbered by V. rumoiensis or Brochothrix thermosphacta. The microbiota of spoiled and R samples could not be clustered on the basis of specific defects (discoloration, presence of slime, sourness, and swollen packages) or supplemented additives. LAB population of S samples, averaging 2.9 log10(cfu/g), increased to 7.7 log10(cfu/g) in the E and R samples. Dominant cultivable LAB belonged to the species Lactobacillus sakei and Leuconostoc carnosum. The same biotypes ascribed to different species where often found in the corresponding S and R samples, and sometime in different batches provided from the same producer, suggesting a recurrent contamination from the plant of production. Consistently with growth of LAB, initial pH (6.26) dropped to 5.74 in E samples. Volatiles organic compound (VOCs) analysis revealed that ethanol was the major metabolite produced during the shelf life. The profile of volatile compounds got enriched with other molecules (e.g. 2-butanone, ethyl acetate, acetic acid, acetoin, butanoic acid, ethyl ester, butanoic acid, and 2,3-butanediol) mainly ascribed to microbial metabolism.

Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors.

Hyperspectral sensors represent a powerful tool for chemical mapping of solid-state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real-time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three-dimensional data array corresponding to each hyperspectral image into a one-dimensional signal (1D-DR), which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D-DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D-DR methods: average spectrum (AS), single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH). In particular, we have considered 370 NIR-hyperspectral images of a set of green coffee samples, and the three 1D-DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares-discriminant analysis were used to compare the three separate DR methods. Furthermore, low-level and mid-level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical Abstract Key steps in hyperspectral data dimenionality reduction.

Determination of polyphenol content and colour index in wines through PEDOT-modified electrodes.

Poly(3,4-ethylenedioxythiophene)-modified electrodes have been used for the estimation of the polyphenolic content and of the colour index of different samples of wines. Synthetic wine solutions, prepared with different amount of oenocyanins, have been analysed spectrophotometrically and electrochemically in order to find a correlation between the total polyphenolic content or colour index and the current peak. The regression curves obtained have been used as external calibration lines for the analysis of several commercial wines, ranging from white to dark red wines. In this way, a rapid estimation of the total polyphenolic content and of the colour index may be accomplished from a single voltammetric measurement. Furthermore, principal component analysis has also been used to evaluate the effect of total polyphenolic content and colour index on the whole voltammetric signals within a selected potential range, both for the synthetic solutions and for the commercial products. Graphical abstract Electrochemical sensors for the rapid determination of colour index and polyphenol content in wines.

The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria.

Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest. In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham. Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470-5880cm(-1)) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500-5800cm(-1) region, and a FT-MIR microscopy operating in the 4000-675cm(-1) region. Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

Handling large datasets of hyperspectral images: reducing data size without loss of useful information.

Hyperspectral Imaging (HSI) is gaining increasing interest in the field of analytical chemistry, since this fast and non-destructive technique allows one to easily acquire a large amount of spectral and spatial information on a wide number of samples in very short times. However, the large size of hyperspectral image data often limits the possible uses of this technique, due to the difficulty of evaluating many samples altogether, for example when one needs to consider a representative number of samples for the implementation of on-line applications. In order to solve this problem, we propose a novel chemometric strategy aimed to significantly reduce the dataset size, which allows to analyze in a completely automated way from tens up to hundreds of hyperspectral images altogether, without losing neither spectral nor spatial information. The approach essentially consists in compressing each hyperspectral image into a signal, named hyperspectrogram, which is created by combining several quantities obtained by applying PCA to each single hyperspectral image. Hyperspectrograms can then be used as a compact set of descriptors and subjected to blind analysis techniques. Moreover, a further improvement of both data compression and calibration/classification performances can be achieved by applying proper variable selection methods to the hyperspectrograms. A visual evaluation of the correctness of the choices made by the algorithm can be obtained by representing the selected features back into the original image domain. Likewise, the interpretation of the chemical information underlying the selected regions of the hyperspectrograms related to the loadings is enabled by projecting them in the original spectral domain. Examples of applications of the hyperspectrogram-based approach to hyperspectral images of food samples in the NIR range (1000-1700 nm) and in the vis-NIR range (400-1000 nm), facing a calibration and a defect detection issue respectively, demonstrate the effectiveness of the proposed approach.

Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques.

In the present paper, the possibility to use the information contained in RGB digital images to gain a fast and inexpensive quantification of colour-related properties of food is explored. To this aim, we present an approach which consists, as first step, in condensing the colour related information contained in RGB digital images of the analysed samples in one-dimensional signals, named colourgrams. These signals are then used as descriptor variables in multivariate calibration models. The feasibility of this approach has been tested using as a benchmark a series of samples of pesto sauce, whose RGB images have been used to predict both visual attributes defined by a panel test and the content of various pigments (chlorophylls a and b, pheophytins a and b, ß-carotene and lutein). The possibility to predict correctly the values of some of the studied parameters suggests the feasibility of this approach for fast monitoring of the main aspect-related properties of a food matrix. The values of the squared correlation coefficient computed in prediction on a test set (R(Pred)(2)) for green and yellow hues were greater than 0.75, while R(Pred)(2) values greater than 0.85 were obtained for the prediction of total chlorophylls content and of chlorophylls/pheophytins ratio. The great flexibility of this blind analysis method for the quantitative evaluation of colour related features of matrices with an inhomogeneous aspect suggests that it is possible to implement automated, objective, and transferable systems for fast monitoring of raw materials, different stages of the manufacture and end products, not necessarily for the food industry only.

Investigation on a Roman copper alloy artefact from Pompeii (Italy).

A selection of samples, obtained from a particular copper-alloy domestic artefact of Roman style from Pompeii, has been analysed by using different techniques (IR, Raman, SEM-EDX, FAAS), in order to investigate the chemical nature and composition of the metals utilised for such manufacturing pieces. The surface analysis of the bright red metallic microfragments conducted by different analytical techniques, emphasises the presence of pure unalloyed copper and confirms the absence of other metallic species on the upper layers. On the contrary, the mapping analysis of the section of the laminar metal of the investigated sample shows a consistent enrichment in tin content. Finally, destructive analysis by FAAS confirms that the artefact looks like a bronze metal alloy, with a medium Sn content of about 6.5%.

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration.

In the present work, we explored the possibility of using near-infrared spectroscopy in order to quantify the degree of adulteration of durum wheat flour with common bread wheat flour. The multivariate calibration techniques adopted to this aim were PLS and a wavelet-based calibration algorithm, recently developed by some of us, called WILMA. Both techniques provided satisfactory results, the percentage of adulterant present in the samples being quantified with an uncertainty lower than that associated to the Italian official method. In particular the WILMA algorithm, by performing feature selection, allowed the signal pretreatment to be avoided and obtaining more parsimonious models.

Use of multivariate analysis of MIR spectra to study bread staling.

Different kinds of bread, stored at constant temperature and at controlled humidity conditions for a week since their manufacturing date, were analysed by Attenuated Total Reflectance-Fourier Transform InfraRed (ATR-FTIR) spectroscopy. The collected spectra were processed by Principal Component Analysis (PCA), in order to evaluate the changes occurring during bread ageing. For the sake of comparison, the 1060-950 cm spectral window has been also investigated by curve-fitting methods. It was observed that the first PC increases monotonically with ageing of samples. Furthermore, the more influential variables on PC1 correspond to spectral regions where are located stretching and bending bands, which are mainly attributed to typical starch bonds vibrations.

Classification of cereal flours by chemometric analysis of MIR spectra.

Different kinds of cereal flours submitted to various technological treatments were classified on the basis of their mid-infrared spectra by pattern recognition techniques. Classification in the wavelet domain was achieved by using the wavelet packet transform for efficient pattern recognition (WPTER) algorithm, which allowed singling out the most discriminant spectral regions. Principal component analysis (PCA) on the selected features showed an effective clustering of the analyzed flours. Satisfactory classification models were obtained both on training and test samples. Furthermore, mixtures of varying composition of the studied flours were distributed in the PCA space according to their composition.