Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable “fingerprints.” Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10(7) bacteria·g(−1) the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels

Study of the optimal waveforms for non-destructive spectral analysis of aqueous solutions by means of audible sound and optimization algorithms

Acoustic analysis of materials is a common non-destructive technique, but most efforts are focused on the ultrasonic range. In the audible range, such studies are generally devoted to audio engineering applications. Ultrasonic sound has evident advantages, but also severe limitations, like penetration depth and the use of coupling gels. We propose a biomimetic approach in the audible range to overcome some of these limitations. A total of 364 samples of water and fructose solutions with 28 concentrations between 0 g/L and 9 g/L have been analyzed inside an anechoic chamber using audible sound configurations. The spectral information from the scattered sound is used to identify and discriminate the concentration with the help of an improved grouping genetic algorithm that extracts a set of frequencies as a classifier. The fitness function of the optimization algorithm implements an extreme learning machine. The classifier obtained with this new technique is composed only by nine frequencies in the (3–15) kHz range. The results have been obtained over 20,000 independent random iterations, achieving an average classification accuracy of 98.65% for concentrations with a difference of ±0.01 g/L

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Vibroscape analysis reveals acoustic niche overlap and plastic alteration of vibratory courtship signals in ground-dwelling wolf spiders

To expand the scope of soundscape ecology to encompass substrate-borne vibrations (i.e. vibroscapes), we analyzed the vibroscape of a deciduous forest floor using contact microphone arrays followed by automated processing of large audio datasets. We then focused on vibratory signaling of ground-dwelling Schizocosa wolf spiders to test for (i) acoustic niche partitioning and (ii) plastic behavioral responses that might reduce the risk of signal interference from substrate-borne noise and conspecific/heterospecific signaling. Two closely related species - S. stridulans and S. uetzi - showed high acoustic niche overlap across space, time, and dominant frequency. Both species show plastic behavioral responses - S. uetzi males shorten their courtship in higher abundance of substrate-borne noise, S. stridulans males increased the duration of their vibratory courtship signals in a higher abundance of conspecific signals, and S. stridulans males decreased vibratory signal complexity in a higher abundance of S. uetzi signals

Spectrochemical analysis of liquid biopsy harnessed to multivariate analysis towards breast cancer screening

Mortality due to breast cancer could be reduced via screening programs where preliminary clinical tests employed in an asymptomatic well-population with the objective of identifying cancer biomarkers could allow earlier referral of women with altered results for deeper clinical analysis and treatment. The introduction of well-population screening using new and less-invasive technologies as a strategy for earlier detection of breast cancer is thus highly desirable. Herein, spectrochemical analyses harnessed to multivariate classification techniques are used as a bio-analytical tool for a Breast Cancer Screening Program using liquid biopsy in the form of blood plasma samples collected from 476 patients recruited over a 2-year period. This methodology is based on acquiring and analysing the spectrochemical fingerprint of plasma samples by attenuated total reflection Fourier-transform infrared spectroscopy; derived spectra reflect intrinsic biochemical composition, generating information on nucleic acids, carbohydrates, lipids and proteins. Excellent results in terms of sensitivity (94%) and specificity (91%) were obtained using this method in comparison with traditional mammography (88–93% and 85–94%, respectively). Additional advantages such as better disease prognosis thus allowing a more effective treatment, lower associated morbidity, fewer false-positive and false-negative results, lower-cost, and higher analytical frequency make this method attractive for translation to the clinical setting

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Fucosylated and Sulfated Glycans Investigated using Cryogenic Infrared Spectroscopy

Unusual monosaccharides (fucose), covalent modifications of glycans (sulfation) and terminal sequences play important biological roles in physiology and pathology of living organisms. Furthermore, in an evolutionary sense, uncommon structures are often the result of selection pressures and can be the source to a deeper understanding of the evolution of glycosylation.157 At the same time, fucosylated glycans and sulfated glycans still challenge standard mass spectrometry (MS)-based analytical workflows in glycan analysis. MS emerged throughout the last decade as the most widely used analytical technique in glycan analysis. As a stand-alone technique, it is limited in glycan analysis due to the presence of isomers. Isomerism in glycans arises from their composition, connectivity, configuration, and branching. Therefore, MS is often coupled to orthogonal techniques such as liquid chromatography (LC) and ion mobility spectrometry (IM-MS). Most recently, the combination of cryogenic IR spectroscopy in the gas phase with MS proved beneficial for the identification of smaller glycans. At low measurement temperatures, the IR spectrum of small glycans provides a unique fingerprint to the underlying chemical structure and conformation.In this thesis, cryogenic IR spectroscopy as an addition to the MS-based analytical toolbox was used to shed light on the migration of fucose residues in MS experiments. This elusive rearrangement reaction is not restricted to tandem MS workflows but is recently found to occur in intact ions without extensive activation. Here, the role of the proton in fucose migration reactions was investigated for the two glycan epitopes Lewis x and blood group H type 2. A systematic study of adduct ions and functional groups with competing proton affinities demonstrated that the proton can be selectively mobilized and demobilized. Planning MS-based experiments of fucosylated glycan cations certainly needs an effective strategy to circumvent the presence of a mobile proton in order to avoid erroneous sequence assignments.In a multidimensional approach, IR spectroscopy, IM-MS, RDD and computational modelling were combined to decode the rearrangement product and the reaction mechanism. The trisaccharides Lewis x and blood group H type 2 were found to migrate to a third chemical structure, in which the fucose moiety is most likely 1,6-linked to galactose. The barrier is much higher for blood group H type 2 compared to Lewis x and it is feasible that the latter is never detected in its original chemical structure in the mass spectrometer. These results generalize fucose migration to a universal issue in any mass spectrometer to which even various orthogonal MS-based techniques can be blind.In the second part of this thesis, cryogenic IR spectroscopy in combination with computational modelling was employed for the structural analysis of sulfated glycosaminoglycans (GAGs). Diversity in the chemical structure of linear and acidic GAGs arises from the GAG class, sulfation, epimerization and acetylation. Using messenger tagging IR spectroscopy, sulfated mono- and disaccharides have been characterized successfully recently. In the present thesis, the prominent anticoagulant pentasaccharide fondaparinux which carries eight sulfate functional groups was investigated using cryogenic IR spectroscopy in helium nanodroplets as a proof-of-concept. The spectroscopic fingerprint features unique absorption bands in the mid-IR range for the sulfate functional groups. With this knowledge, a systematic set of all naturally occurring sulfation variations in chondroitin and dermatan sulfate (CS/DS) further demonstrated the capabilities of cryogenic IR spectroscopy for their differentiation. Moreover, from their IR fingerprints in combination with computational modelling, conformational diversity arising from sulfation and charge density distribution could be derived. In a different study, the IR fingerprints of four heparan sulfate (HS) diastereomers revealed a modularity in their chemical structure which was explained, using computational modelling, from their unique hydrogen bonding patterns. The knowledge of the preferred hydrogen bonding pattern could aid e.g. the development for labelling strategies in IM-MS. The results show that the high resolution in the optical fingerprints of GAGs allows to unambiguously resolve their diversity arising from GAG class, sulfation and epimerization. The results exemplify the importance of gas- phase cryogenic IR spectroscopy to enhance future analytical workflows for GAG sequencing. A fully MS-based workflow could involve the ionization of an intact GAG chain and combine tandem MS with IM-MS and cryogenic IR spectroscopy of respective fragments to unambiguously characterize a GAG chain in a single MS experiment.In the last part, cryogenic IR spectroscopy was combined with random forest modelling to extract vibrational features that are characteristic to structural features in GAGs. The selected structural features included the GAG class and sulfation and therefore, almost fully characterize the underlying chemical structure. In a proof-of-concept study, a prediction score of >97% could be achieved for HS tetra- and hexasaccharides based on a training set of only 21 spectra. Especially for certain marker motifs, such as 3-O-sulfation in cancer cells, this workflow could prove beneficial. With machine learning algorithms, the need for comprehensive spectral databases could be circumvented for the identification of unknowns. Overall, the results show that MS-based IR spectroscopy certainly has the potential to leave the framework of academic basic research and add as a valuable addition to the MS-based analytical toolbox.Weinig voorkomende monosachariden (fucose), covalente modificaties van glycanen (sulfering) en terminale sequenties spelen belangrijke rollen in de fysiologie en pathologie van levende organismen. Weinig voorkomende structuren zijn in evolutionaire zin vaak het resultaat van selectiedruk en kunnen derhalve een dieper inzicht leveren in de evolutie van glycosylering. Gefucosyleerde glycanen en gesulfoneerde glycanen vormen echter nog steeds een uitdaging voor standaard workflows in glycaananalyse. Massaspectrometrie (MS) heeft zich in het laatste decennium ontwikkeld tot de meest gebruikte techniek voor glycaananalyse, maar is beperkt door de aanwezigheid van isomeren. Isomeren van glycanen zijn het gevolg van hun samenstelling, connectiviteit, configuratie en vertakking. MS wordt daarom vaak gekoppeld aan complementaire technieken zoals vloeistofchromatografie (LC) en ion- mobiliteitsspectrometrie (IM-MS). Gedurende de laatste jaren is de combinatie van cryogene infrarood (IR)-spectroscopie in de gasfase met MS van grote waarde gebleken voor de identificatie van kleinere glycanen. Bij lage meettemperaturen geeft het IR spectrum van kleine glycanen een unieke vingerafdruk van de onderliggende chemische structuur en conformatie.In dit proefschrift is cryogene IR-spectroscopie in combinatie met MS- gebaseerde analytische technieken gebruikt om licht te werpen op de migratie van fucose in MS-experimenten. Deze ongrijpbare migratiereactie is niet beperkt tot tandem MS workflows, maar is recentelijk ook waargenomen in intacte ionen zonder uitgebreide activering. De rol van het proton in fucose- migratiereacties is onderzocht voor de twee glycaanepitopen Lewis x en bloedgroep H type 2. In een systematische studie van adductie-ionen en functionele groepen met concurrerende protonaffiniteiten is aangetoond dat het proton selectief gemobiliseerd en gedemobiliseerd kan worden. Het meten van gefucosyleerde glycaan-kationen met MS vereist een effectieve strategie om de aanwezigheid van een mobiel proton te omzeilen om foutieve sequentie- toewijzingen te voorkomen.In een multidimensionele benadering zijn IR spectroscopie, IM-MS, radical- directed dissociation (RDD) MS en computationele modellering gecombineerd om het migratieproduct en het reactiemechanisme te ontcijferen. De trisachariden Lewis x en bloedgroep H type 2 blijken te migreren naar een chemische structuur, waarin fucose hoogstwaarschijnlijk 1,6-gekoppeld is aan galactose. De barriËre is veel hoger voor bloedgroep H type 2 dan voor Lewis x en het is goed mogelijk dat de laatste nooit in zijn oorspronkelijke chemische structuur gedetecteerd is in de massaspectrometer. Uit deze resultaten blijkt dat fucose-migratie een universeel probleem is in elke massaspectrometer en dat ook het gebruik van verschillende complementaire MS-gebaseerde technieken dit probleem niet geheel kan oplossen.In het tweede deel van dit proefschrift is cryogene IR spectroscopie in combinatie met computationele modellering gebruikt voor de structurele analyse van gesulfoneerde glycosaminoglycanen (GAG9s). De verscheidenheid in de chemische structuur van lineaire zure GAG9s komt voort uit de GAG klasse, sulfatie, epimerisatie en acetylatie. Met behulp van messenger tagging IR spectroscopie zijn recentelijk met succes gesulfoneerde mono- en disachariden gekarakteriseerd. In dit proefschrift is het anticoagulant pentasaccharide fondaparinux, dat acht sulfaatgroepen bevat, onderzocht met behulp van cryogene IR spectroscopie in helium nanodruppels om het werkingsprincipe van de meting aan te tonen. De spectroscopische vingerafdruk toont unieke absorptiebanden in het midden-IR bereik voor de sulfaatgroepen. Het meten van een systematische set van alle natuurlijk voorkomende sulfatievariaties in chondroÔtine- en dermatan-sulfaat (CS/DS) heeft de differentiatie mogelijkheden met behulp van cryogene IR spectroscopie verder aangetoond. Uit de IR-vingerafdruk in combinatie met computationele modellering kan bovendien conformationele diversiteit als gevolg van sulfatie en ladingsdichtheidsverdeling worden afgeleid. In een andere studie onthullen de IR-vingerafdrukken van vier heparansulfaat (HS) diastereomeren een modulariteit in hun chemische structuur die verklaard is met behulp van computationele modellering door hun unieke waterstofbrugpatronen. De kennis van het geprefereerde waterstofbindingspatroon zou bijvoorbeeld kunnen helpen bij de ontwikkeling van labelingstrategieÎn in IM-MS. De resultaten laten zien dat de hoge resolutie in de optische vingerafdrukken van GAG9s het mogelijk maakt om eenduidig de diversiteit op te lossen dievoortkomt uit GAG klasse, sulfatie en epimerisatie. De resultaten illustreren het belang van gas-fase cryogene IR spectroscopie om toekomstige analytische workflows voor GAG sequencing te verbeteren. Een volledig op MS gebaseerde workflow zou de ionisatie van een intacte GAG-keten kunnen omvatten en tandem MS met IM-MS en cryogene IR-spectroscopie van de respectieve fragmenten kunnen combineren om een GAG-keten eenduidig te karakteriseren in ÈÈn enkel MS-experiment.In het laatste deel van het proefschrift is cryogene IR-spectroscopie gecombineerd met random forest modellering om vibratie patronen die kenmerkend zijn voor structurele eigenschappen in GAG9s aan te tonen. De geselecteerde structurele eigenschappen omvatten de GAG-klasse en sulfatie en karakteriseren derhalve bijna volledig de onderliggende chemische structuur. In een proof-of-concept studie is een voorspellingsscore van >97% bereikt voor HS tetra- en hexasachariden op basis van een trainingsset van slechts 21 spectra. Vooral voor bepaalde markermotieven, zoals 3-O-sulfatie in kankercellen, zou deze workflow nuttig kunnen blijken. Met algoritmen voor machine learning zou de noodzaak voor het gebruik van uitgebreide spectrale databanken voor de identificatie van onbekende GAG9s kunnen worden omzeild. Concluderend kan gesteld worden dat de resultaten zoals beschreven in dit proefschrift aantonen dat IR-spectroscopie op basis van MS zeker het potentieel heeft om het stadium van het academisch basisonderzoek te verlaten en een waardevolle aanvulling vormt op MS gebaseerde analytische technieken

Raman Fingerprints of SARS-CoV‐2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machinelearning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman “metabolomic snapshot”, which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses