Effects of time and temperature of storage on chemical and nutritional characteristics of raw milk for Provolone Valpadana PDO cheesemaking: a multivariate approach.

We evaluated the possibility of increasing the storage temperature of raw milk for Provolone Valpadana cheesemaking, to identify the most suitable conditions of time and temperature for a pre-maturation process. We used Principal Component Analysis (PCA) to analyze the overall effects of different storage conditions on chemical, nutritional and technological characteristics of the raw milk. Four different thermal storage cycles, two at fixed temperature/time (6 and 12°C for 60 h) and two with two-phase thermal cycle (10 and 12°C for 15 h, followed by refrigeration at 4°C for 45 h) were studied. Although a moderate heterogeneity among raw milks from the 11 producers of Provolone Valpadana cheese was observed, PCA revealed the critical aspects of the extreme storage conditions (60 h of refrigeration). Some samples resulted in anomalous behaviors, probably related to unexpected fermentation phenomena occurring with increasing storage temperature. The acidification and the increase in the contents of lactic acid, soluble calcium, and degree of retinol isomerization observed in the anomalous samples can compromise the technological functionality of milk. Conversely, the storage with a two-phase thermal cycle did not lead to variations in any measured characteristic, suggesting that mild refrigeration conditions (10 or 12°C for 15 h followed by 4°C for 45 h) could be a good compromise in favoring milk pre-maturation without altering its quality characteristics.

Comparative study of in-vitro autofluorescence of normal versus non-melanoma-skin-cancer cells at different excitation wavelengths.

In this experimental study the autofluorescence of squamous carcinoma cells, stimulated by 6 different excitation wavelengths in the range 280-533 nm, has been compared with the autofluorescence of normal control keratinocytes. Skin cells were cultivated in vitro, to isolate their characteristic autofluorescence form the more complex one that would be originated by the complete skin tissue. Autofluorescence spectra in the visible range were complemented by absorption measurements. It was observed that the control cells showed characteristic emission (and absorption) structures due to typical endogenous chromophores [FAD and NAD(P)H, lipo-pigments, porphyrins], that were severely dumped in pathological cells. The autofluorescence spectra were then elaborated by multivariate analysis: after a first exploratory data analysis by means of Principal Component Analysis, the whole dataset was used to develop classification models using partial least squares-discriminant analysis, to differentiate between normal and pathological cells. This permitted us to identify the most suitable fluorescence spectral interval, in the 550-670 nm range, to discriminate between normal and pathological behavior, independently on the excitation wavelength.

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.

Tracing geographical origin of Lambrusco PDO wines using isotope ratios of oxygen, boron, strontium, lead and their elemental concentration.

Wine identification is one of the most important aspects in the classification of wines and consumer protection. In particular, assuring wine authenticity is a crucial issue on which researchers are focusing on. This study aims to evaluate the feasibility of using chemical (B, Pb and Sr concentration) and isotopic compositions (δ11B, 20yPb/20xPb, 87Sr/86Sr and δ18O) of wine samples to trace their geographic origins. Different PDO Lambrusco wines coming from a confined area of northern Italy were analyzed and all the isotopic systematics were monitored by using a multi collector inductively coupled plasma mass spectrometer (MC-ICP/MS). The obtained results showed that boron isotope ratio measurements led to a satisfactory degree of accuracy and precision (measured value, n = 28, 11B/10B of NIST SRM 951a equal to 4.04343 ± 0.00178, (u = 2s) with a certified value of 4.04362 ± 0.00136 (u = 2s). Furthermore, in the present study, it has been possible to highlight significant differences among samples by means of one-way analysis of variance (ANOVA) and post hoc Tukey-Kramer test. Finally, Principal Component Analysis (PCA) was also carried out in order to evaluate to which extent the different PDOs can be distinguished from each other, taking into account the whole set of geographical origin descriptors. Although δ11B provided more sensitive information, the obtained results highlighted the important to consider the synergistic combination of all the investigated parameters to trace the different samples and the need to combine the obtained values with the same parameters evaluated in the soil, water and fertilizer as well.

Analysis of Housing Risk Factors for the Welfare of Lean and Heavy Pigs in a Sample of European Fattening Farms.

Pig welfare is affected by housing conditions, the minimum requirements of which are set up by EU legislation. Animal and non-animal-based measures are useful indicators to investigate housing risk factors for pig welfare. An observational study on 51 pig farms in seven EU countries, aimed at investigating housing risk factors for the welfare of finishing pigs, showed body weight and presence of bedded solid floored resting area (BED) identifying three clusters of farms. Farms with BED were featured by no or limited tail docking, larger availability of manipulable materials and lower number of pigs per farm and per annual work unit. In these farms, less skin and ear lesions were found, compared with lean pigs of farms without BED, which were characterized by lower pig space allowance, mortality rate and medication cost. In farms without BED, heavy pigs were featured by more space per pig, more pigs per drinker and higher mortality rate and medication cost per pig, compared to lean pigs. No statistical difference in tail lesions was found between the three farm clusters, although tail docking was performed in all farms without BED and not performed on most farms with BED.

Multivariate Analysis in Microbiome Description: Correlation of Human Gut Protein Degraders, Metabolites, and Predicted Metabolic Functions.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia-Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

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.

Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs) - a feasibility study.

From a circular economy perspective, feeding livestock with food leftovers or former foodstuff products (FFPs) could be an effective option aimed at exploiting food leftover resources and reducing food losses. FFPs are valuable energy sources, characterised by a beneficial starch/sugar content, and also fats. However, besides these nutritional aspects, safety is a key concern given that FFPs are generally derived from packaged food. Packaging materials, such as plastics and paper, are not accepted as a feed ingredient which means that residues should be rigorously avoided. A sensitive and objective detection method is thus essential for an accurate risk evaluation throughout the former food production chain. To this end, former food samples were collected in processing plants of two different European countries and subjected to multivariate analysis of red, green, and blue (RGB) microscopic images, in order to evaluate the possible application of this non-destructive technique for the rapid detection of residual particles from packaging materials. Multivariate Image Analysis (MIA) was performed on single images at the pixel level, which essentially consisted in an exploratory analysis of the image data by means of Principal Component Analysis, which highlighted the differences between packaging and foodstuff particles, based on their colour. The whole dataset of images was then analysed by means of a multivariate data dimensionality reduction method known as the colourgrams approach, which identified clusters of images sharing similar features and also highlighted outlier images due to the presence of packaging particles. The results obtained in this feasibility study demonstrated that MIA is a promising tool for a rapid automated method for detecting particles of packaging materials in FFPs.

Land Snails as a Valuable Source of Fatty Acids: A Multivariate Statistical Approach.

The fatty acid (FA) profile of wild Theba pisana, Cornu aspersum, and Eobania vermiculata land snail samples, collected in Sicily (Southern Italy), before and after heat treatment at +100 °C were examined by gas chromatography with a flame ionization detector (GC-FID). The results show a higher content of polyunsaturated fatty acids (PUFAs) in all of the examined raw snails samples, representing up to 48.10% of the total fatty acids contents, followed by monounsaturated fatty acids (MUFAs). The thermal processing of the snail samples examined determined an overall reduction of PUFA levels (8.13%, 7.75%, and 4.62% for T. pisana, C. aspersum and E. vermiculata samples, respectively) and a species-specific variation of saturated fatty acid (SFA) contents. Oleic acid remained the most abundant FA of all of the snails species examined, accounting for up to 29.95% of the total FA content. A relevant decrease of É·3/É·6 ratio was found only for T. pisana samples. The principal component analysis (PCA) showed a separation of the snail samples in terms of species and heat treatment. The results of this work suggest land snails as a valuable source of MUFA and PUFA contents and boiling as appropriate treatment, according to the maintenance of healthy properties.

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.

Bacterial community of industrial raw sausage packaged in modified atmosphere throughout the shelf life.

Ten lots of industrial raw sausages in modified atmosphere (CO2 30%, O2 70%), produced in the same plant over 7 months, were analyzed at the day after production (S samples) and at the end of shelf life (E samples), after 12 days storage at 7 °C to simulate thermal abuse. Quality of the products was generally compromised by storage at 7 °C, with only 3 E samples without alterations. During the shelf life, the pH decreased for the accumulation of acetic and lactic acids. A few biogenic amines accumulated, remaining below acceptable limits. The profile of volatile compounds got enriched with alcohols, ketones, and acids (e.g. ethanol, 2,3-butanediol, 2,3-butandione, butanoic acid) originated by bacterial metabolism. Throughout the shelf life, aerobic bacteria increased from 4.7 log to 6.6 log cfu/g, and lactic acid bacteria (LAB) from 3.7 to 8.1 log cfu/g. Staphylococci, enterobacteria, and pseudomonads passed from 3.7, 3.0, and 1.7 to 5.5, 4.8, and 3.0 log cfu/g, respectively. Dominant cultivable LAB, genotyped by RAPD-PCR, belonged to the species Lactobacillus curvatus/graminis and Lactobacillus sakei, with lower amounts of Leuconostoc carnosum and Leuconostoc mesenteroides. Brochothrix thermosphacta was the prevailing species among aerobic bacteria. The same biotypes ascribed to several different species where often found in E samples of diverse batches, suggesting a recurrent contamination from the plant of production. Profiling of 16S rRNA gene evidenced that microbiota of S samples clustered in two main groups where either Firmicutes or Bacteroidetes prevailed, albeit with taxa generally associated to the gastro-intestinal tract of mammals. The microbial diversity was lower in E samples than in S ones. Even though a common profile could not be identified, most E samples clustered together and were dominated by Firmicutes, with Lactobacillaceae and Listeriaceae as the most abundant families (mostly ascribed to Lactobacillus and Brochothrix, respectively). In a sole E sample Proteobacteria (especially Serratia) was the major phylum.

Characterization of the peptide fraction from digested Parmigiano Reggiano cheese and its effect on growth of lactobacilli and bifidobacteria.

Parmigiano Reggiano (PR) is a raw-milk, hard cooked, long-ripened cheese of high quality and nutritional value. Long ripening times allow for extensive proteolysis of milk proteins to yield a number of peptides, some of which have potential healthy bioactive properties. This study aimed to: i) determine the peptide profile of PR cheese subjected to simulated gastrointestinal transit; ii) evaluate in vitro whether the peptides could support growth of beneficial microbial groups of the gut microbiota. PR samples were subjected to in vitro digestion, simulating oral, gastric, and duodenal transit. Liquid chromatography coupled with tandem mass spectrometry revealed that digestion caused the disappearance of the serum proteins and most of the original peptides, while 71 new peptides were found, all ranging from 2 to 24 residues. The digests were given as sole nitrogen source to pure cultures of Bifidobacterium (27 strains) and Lactobacillus (30 strains), and to bioreactor batch cultures of human gut microbiota. Most of bifidobacteria and lactobacilli grew more abundantly on PR digests than on the control peptone, and exhibited strain- or species-specific peptide preferences, as evidenced by principal component analysis. Bifidobacteria generally consumed a greater amount of peptides than lactobacilli, in terms of both the mean peptide consumption and the number of peptides consumed. For bifidobacteria, peptide preferences were very diverse, but a core of 10 peptides with 4 or 5 residues were consumed by all the strains. Lactobacilli behaved more homogenously and consumed nearly only the same 6 peptides, mostly dipeptides. The peptide preferences of the different groups of bifidobacteria and lactobacilli could not be ascribed to features such as the length of the peptide or the abundance of residues with peculiar properties (hydrophobicity, polarity, charge) and likely depend on specific proteases and/or peptide transporters preferentially recognizing specific sequence motifs. The cultures of human colonic microbiota confirmed that PR digest promoted the growth of commensal bifidobacteria. This study demonstrated that peptides derived from simulated gastrointestinal digestion of PR supported the growth of most lactobacilli and bifidobacteria.

Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee.

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

Calculating the iodine value for Italian heavy pig subcutaneous adipose tissue from fatty acid methyl ester profiles.

In this work, different equations were compared as for their effectiveness in predicting the iodine value (IV), based on fatty acid (FA) composition of subcutaneous adipose tissue of Italian heavy pigs. In particular, six equations were tested: AOCS (1); modified AOCS (2), including all unsaturated FA (UFA); regression models obtained using the stepwise regression procedure as variable selection method, calculated considering only UFA (3) or all the FA (4); regression models obtained using the backward elimination procedure, calculated considering only UFA (5) or all the FA (6). The comparison of the equations performance, estimated using an external test set, showed that the use of regression models led to significant enhancements of prediction accuracy with respect to the AOCS equations. Using both equations 4 and 6, the average paired differences between experimental and predicted IV values were not statistically significant. Therefore, it is possible to use these equations for IV estimation of the subcutaneous adipose tissue of Italian heavy pigs.

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.

Mining metagenomic whole genome sequences revealed subdominant but constant Lactobacillus population in the human gut microbiota.

The genus Lactobacillus includes over 215 species that colonize plants, foods, sewage and the gastrointestinal tract (GIT) of humans and animals. In the GIT, Lactobacillus population can be made by true inhabitants or by bacteria occasionally ingested with fermented or spoiled foods, or with probiotics. This study longitudinally surveyed Lactobacillus species and strains in the feces of a healthy subject through whole genome sequencing (WGS) data-mining, in order to identify members of the permanent or transient populations. In three time-points (0, 670 and 700 d), 58 different species were identified, 16 of them being retrieved for the first time in human feces. L. rhamnosus, L. ruminis, L. delbrueckii, L. plantarum, L. casei and L. acidophilus were the most represented, with estimated amounts ranging between 6 and 8 Log (cells g(-1) ), while the other were detected at 4 or 5 Log (cells g(-1) ). 86 Lactobacillus strains belonging to 52 species were identified. 43 seemingly occupied the GIT as true residents, since were detected in a time span of almost 2 years in all the three samples or in 2 samples separated by 670 or 700 d. As a whole, a stable community of lactobacilli was disclosed, with wide and understudied biodiversity.

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.