Self-learning modules for spectra evaluation

Monitoring milk processing is an essential task as it affects the quality and safety of the final product. The aim of this investigation was to develop and analyse the self-learning system for the supervision of the processing of milk. In the self-learning evaluation module, several algorithms for data analysis of near infrared (NIR) and Raman spectra was implemented for the prediction of sample quality and safety. In the first part of this thesis, the use of NIR spectroscopy for controlling milk processing was investigated. For this reason, a high-quality quartz flow cell with a 1 mm pathlength including temperature controlling option for liquids was implemented. For sample preparation, UHT-milk with 1.5 % fat content was measured at 5 °C and considered as the reference milk. Samples with various changes such as added water and cleaning solution, different fat content and temperature as well as milks from various suppliers were investigated as the modified samples. A data set from reference and modified samples was obtained with NIR measurements. In this study, first Savitzky-Golay derivative with second polynomial order and window size of 15 was applied. It was compared with the usefulness of raw spectrum and also the combination of raw and first derivative spectrum. For the self-learning sector, an autoencoder neural network was employed. Within this thesis, it was shown that the trained autoencoder using first derivative spectra was capable to detect 5 % added water and 9 % cleaning solution in the milk. However, by using the combination spectra, the difference of 0.1 % in fat concentration was perfectly recognized. These two procedures were able to detect milks from different suppliers and difference of 10 °C in the measurement temperature. Another part of this work was done using Raman spectroscopy. The aim of this part was to check if the previous result can be improved. In this step, the circulation method was again employed the same as in the previous part. However, because of the heat introduced to the sample by the laser using in Raman spectroscopy and the length of plastic tubes which can be affected by the temperature of the laboratory, the measurement temperature was kept at 10 °C. 1.5 % fat UHT milk was utilized as the reference sample. Milks with various changes such as different fat contents, various measurement temperatures and added water or cleaning solution were investigated as the modified samples. In this investigation, not only the autoencoder but also some chemometric models were utilized with the purpose of anomaly detection. Principal component analysis (PCA) was investigated to check if the various samples can becategorized separately. In addition, two chemometric modelling techniques such as principal component regression and Gaussian process regression were tested to check the ability for change detection. By using the results obtained by PCA, a sufficient categorization of various samples was not achieved. While the PCR did not present a promising prediction as the related R2 was 0.7, Gaussian process regression with R2 of 0.97 predicted the changes almost perfectly. The trained autoencoder and Gaussian process regression both were able to define 5 % water and cleaning solution, difference of 0.1 % fat content, and variation of 5 °C in the measurement temperature. In comparison between the autoencoder and Gaussian process regression, it should be mentioned that the Gaussian process regression was capable to determine more abnormal signals than the autoencoder, however, it must be trained with all the possible changes. In contrast, the autoencoder can be trained once just with reference signals and used in online monitoring properly. As the final part and to detect which type of anomalies happened during the milk processing, several classification approaches such as linear discriminant analysis, decision tree, support vector machine, and k nearest neighbour were utilized. While decision trees and linear discriminant analysis failed to effectively characterize the various types of anomalies, the k nearest neighbor and support vector machine presented promising results. The support vector machine presented an accuracy of 81.4 % for test set, while the k nearest neighbor showed an accuracy of 84.8 %. As a result, it is reasonable to assume that both algorithms are capable of classifying various groups of data accurately. It can help the milk business figure out whats going wrong during the processing of milk. In general, Raman spectroscopy produced better findings than NIR spectroscopy, because the typical absorption bands of milk components in NIR spectrometers may be impacted by high water absorption combined with substantial light scattering by fat globules. Additionally, the autoencoder as self-learning system was capable of correctly detecting changes during milk processing, however, classification algorithms can aid in obtaining more details.Die Überwachung der Milchverarbeitung ist eine wesentliche Aufgabe, da sie die Qualität und Sicherheit des Endprodukts beeinflusst. Das Ziel dieser Untersuchung war die Entwicklung und Analyse eines selbstlernenden Systems zur Überwachung der Milchverarbeitung. In dem selbstlernenden Auswertungsmodul wurden verschiedene Algorithmen zur Datenanalyse implementiert, um die Qualität und Sicherheit der Proben mit Hilfe spektroskopischer Methoden vorherzusagen. Im ersten Teil dieser Arbeit wurde der Einsatz der Nahinfrarot-Spektroskopie (NIR) zur Kontrolle der Milchverarbeitung untersucht. Zu diesem Zweck wurde eine hochwertige Quarzdurchflusszelle mit einer Schichtdicke von 1 mm und einer Temperiermöglichkeit für Flüssigkeiten eingesetzt. Zur Probenvorbereitung wurde UHT-Milch mit 1,5 % Fettgehalt bei 5 °C gemessen und als Referenzmilch betrachtet. Als modifizierte Proben wurden Proben mit verschiedenen Veränderungen wie Wasser- und Reinigungsmittelzusatz, unterschiedlichem Fettgehalt und Temperatur sowie Milch von verschiedenen Lieferanten untersucht. Mit NIR Messungen wurde ein Datensatz von Referenz- und modifizierten Proben gewonnen. In dieser Studie wurde die erste Savitzky-Golay-Ableitung mit zweiter Polynomordnung und einer Fenstergröße von 15 verwendet. Sie wurde mit der Auswertegüte des Rohspektrums und auch der Kombination aus Roh- und erstem Ableitungsspektrum verglichen. Für den selbstlernenden Bereich wurde ein neuronales Netz als Autoencoder eingesetzt. Im Rahmen dieser Arbeit wurde gezeigt, dass der trainierte Autoencoder unter Verwendung der ersten Ableitung in der Lage war, 5 % zugesetztes Wasser und 9 % Reinigungslösung in der Milch zu erkennen. Durch die Verwendung der Kombinationsspektren wurde auch der Unterschied von 0,1 % in der Fettkonzentration perfekt erkannt. Diese beiden Verfahren waren in der Lage, Milch von verschiedenen Lieferanten und einem Unterschied von 10 °C bei der Messtemperatur zu erkennen. Ein weiterer Teil dieser Arbeit wurde mit der Raman-Spektroskopie durchgeführt. Ziel dieses Teils war es, zu prüfen, ob das vorherige Ergebnis verbessert werden kann. In diesem Schritt wurde wieder die gleiche Zirkulationsmethode wie im vorherigen Teil verwendet. Wegen der Wärme, die durch den Laser bei der Raman-Spektroskopie in die Probe eingebracht wird, und der Länge der Kunststoffrohre, die durch die Temperatur im Labor beeinflusst werden kann, wurde die Messtemperatur jedoch bei 10 °C gehalten. Als Referenzprobe wurde UHT-Milch mit 1,5 % Fett verwendet. Milch mit verschiedenen Veränderungen wie unterschiedlichen Fettgehalten, verschiedenen Messtemperaturen und Zusatz von Wasser oder Reinigungslösung wurde als modifizierte Probe untersucht. In dieser Untersuchung wurden nicht nur der Autoencoder, sondern auch einige chemometrische Modelle zur Erkennung von Anomalien eingesetzt. Die Hauptkomponentenanalyse (PCA) wurde untersucht, um zu prüfen, ob die verschiedenen Proben separat kategorisiert werden können. Darüber hinaus wurden zwei chemometrische Modellierungstechniken wie die Hauptkomponentenregression und die Gaußsche Prozessregression getestet, um die Fähigkeit zur Erkennung von Veränderungen zu prüfen. Mit den Ergebnissen der PCA konnte keine ausreichende Kategorisierung der verschiedenen Proben erreicht werden. Während die Hauptkomponentenregression (PCR) keine vielversprechende Vorhersage lieferte, da das zugehörige R2 bei 0,7 lag, sagte die Gaußsche Prozessregression mit einem R2 von 0,97 die Veränderungen nahezu perfekt voraus. Sowohl der trainierte Autoencoder als auch die Gaußsche Prozessregression waren in der Lage, 5 % Wasser und Reinigungslösung, einen Unterschied von 0,1 % Fettgehalt und eine Variation der Messtemperatur von 5 °C zu detektieren. Im Vergleich von Autoencoder und der Gaußschen Prozessregression ist zu erwähnen, dass die Gaußsche Prozessregression in der Lage war, mehr anormale Signale zu bestimmen als der Autoencoder, allerdings muss sie mit allen möglichen Änderungentrainiert werden. Im Gegensatz dazu muss der Autoencoder nur einmal mit Referenzsignalentrainiert und kann dann für die Online-Überwachung verwendet werden. Als letzter Teil und umzu erkennen, welche Art von Anomalien während der Milchverarbeitung auftraten, wurden verschiedene Klassifizierungsansätze wie lineare Diskriminanzanalyse, Entscheidungsbaum, Support Vector Machine und K Nearest Neighbour verwendet. Während die Entscheidungsbäume und die lineare Diskriminanzanalyse nicht in der Lage waren, die verschiedenen Arten von Anomalien effektiv zu charakterisieren, lieferten die K Nearest Neighbour und die Support Vector Machine Methode vielversprechende Ergebnisse. Die Support Vector Machine wies eine Genauigkeit von 81,4 % für den Testsatz auf, während die K Nearest Neighbour Methode eine Genauigkeit von 84,8 % ergab. Daher kann man davon ausgehen, dass beide Algorithmen in der Lage sind, verschiedene Datengruppen genau zu klassifizieren. Dies kann der Milchwirtschaft helfen, herauszufinden, was bei der Verarbeitung von Milch falsch läuft. Im Allgemeinen lieferte die Raman-Spektroskopie bessere Ergebnisse als die NIR-Spektroskopie, da die typischen Absorptionsbanden der Milchbestandteile in NIR-Spektrometern durch eine hohe Wasserabsorption in Kombination mit einer erheblichen Lichtstreuung durch Fettkügelchen beeinträchtigt werden können. Darüber hinaus war der Autoencoder als selbstlernendes System in der Lage, Veränderungen während der Milchverarbeitung korrekt zu erkennen, jedoch können Klassifizierungsalgorithmen helfen, mehr Details zu erhalten

On the use of in-situ spectroscopic techniques for the study of the provenance of historic ivories

Current protocols for ivory identification are destructive and resource-consuming. The current investigation aimed to develop a classification model based on insitu spectroscopic techniques combined with chemometrics to discriminate between Asian and African ivory on the field. The spectroscopic techniques utilized were Fourier-transform Infrared Spectroscopy (FT-IR) and Fiber Optics Reflectance Spectroscopy (FORS) in the near-infrared region (NIR) combined with chemometric methods of Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). Historic ivories were successfully classified through FT-IR with PCA, and FORS-NIR with PLS-DA, resulting in a True Prediction Rate from 93.028% to 99.020% in African samples and 93.333% to 100.000% in Asian samples. The study demonstrated the potential of FORS-NIR as an investigative tool for ivory investigations. It also illumined the possibility of ivory trade networks of African ivory with the East, and a scientific perspective of the more desirable mechanical properties of African ivory over Asian ivory; Resumo: Os protocolos atuais para identificação de marfim são destrutivos e consomem muitos recursos. A presente investigação teve como objetivo desenvolver um modelo de classificação baseado em técnicas espectroscópicas in situ combinadas com quimiometria para discriminar entre marfim asiático e africano no campo. As técnicas espectroscópicas utilizadas foram a espectroscopia de infravermelho com transformada de Fourier (FT-IR) e espectroscopia de reflectância de fibra óptica (FORS) na região do infravermelho próximo (NIR) combinada com métodos quimiométricos de análise de componentes principais (PCA) e análise discriminante de quadrados mínimos parciais (PLS-DA). Marfins históricos foram classificados com sucesso através de FT-IR com PCA e FORSNIR com PLS-DA, resultando em uma Taxa de Previsão Verdadeira de 93,028% a 99,020% em amostras africanas e 93,333% a 100,000% em amostras asiáticas. O estudo demonstrou o potencial do FORS-NIR como uma ferramenta investigativa para investigações de marfim. Também iluminou a possibilidade de redes de comércio de marfim africano com o Oriente e uma perspectiva científica das propriedades mecânicas mais desejáveis do marfim africano sobre o marfim asiático

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Assessment of DNA degradation in live spermatozoon using laser tweezers raman microspectrometry

Purpose: Sperm nuclear proteins and DNA integrity have been implicated in infertility and treatment failures. High stallion to stallion variability is observed in sperm cryopreservation protocols. The cells are destroyed with harsh chemicals prior to using biochemical assays to test sperm DNA quality. The feasibility of using Raman spectrometry in combination with a laser trap for non-destructive micromanipulation and characterization of DNA damage in motile stallion and human sperm is experimentally investigated in this thesis. Methods: Live stallion sperms were subjected to controlled cellular damage: (a) four grades of chemically induced oxidative stress using Xanthine – Xanthine Oxidase (b) three grades of osmotic stress using PBS and (c) membrane damage using thermal shock. Live human sperm DNA disintegration with time and oxidative stress were explored on fresh, cryopreserved and swim-up categories. The specimens ranged from sub-fertile patients to fertile donors in a limited study. Post-treatment sperms resuspended in sperm media, placed on a quartz coverslip were trapped with a 785 nm, 25 mW laser, using a 1.4 NA, 60X, water immersion microscope objective. A Raman spectrum of a trapped cell was acquired for 20 – 30 seconds. The spectra from 20 – 40 cells from each specimen were analysed in the 630 cm-1 – 1630 cm-1 region using statistical variance and PCA. Results: The Raman spectra from trapped motile sperm head contain intense peaks that did not require smoothing prior to analysis. PCA of the Raman spectra could not resolve the different grades of applied osmotic and oxidative stress in stallion cells. PCA showed high variability between specimens from the same stallion and between stallions, with distinct clustering by ejaculate. Membrane damage study and spectra from extended trapping also showed distinct specimen to specimen difference within and between stallions. Specimen to specimen variability is observed in motility and viability tests on 1000s of stallion cells using CASA and flow cytometry. Human sperms showed some clustering by category, time, stress and motility and appeared more sensitive to the tests than stallion sperms. Conclusions: Raman spectra originate from the dense region of the trapped sperm head and resemble the fingerprint of dense calf thymus DNA. The cells show species specific response to the applied stress/damage. Stallion sperms show high variability between ejaculates that could not be differentiated by stallions. Human cells appear more sensitive to the applied processes. LTRS of live sperms needs further detailed research, cross correlated with other established complementary techniques, to identify spectral bands that are most sensitive to the various grades of induced DNA and membrane damage

Human iPSC Tissue-Engineered Cartilage for Disease Modeling of Skeletal Dysplasia-Causing TRPV4 Mutations

Cartilage is essential to joint development and function. However, there is a variety of cartilage diseases, ranging from developmental (e.g., skeletal dysplasias) to degenerative (e.g., arthritis), in which treatments and therapeutics are lacking. For example, specific point mutations in the ion channel transient receptor potential vanilloid 4 (TRPV4) prevent proper joint development, leading to mild brachyolmia and severe, neonatally lethal metatropic dysplasia. Tissue-engineered cartilage offers an opportunity to elucidate the underlying mechanisms of these cartilage diseases for the development of treatments. Human induced pluripotent stem cells (hiPSCs) are an improved cell source option for cartilage tissue engineering given their minimal donor site morbidity, absence of ethical concerns, and extensive proliferation, differentiation, and gene editing capacities. Unfortunately, previously published hiPSC chondrogenesis protocols were time consuming, difficult to reproduce, and resulted in off-target differentiation. Here, we used two methods to enhance hiPSC chondrogenesis using our previously published stepwise chondrogenic differentiation protocol. Next, we used the improved protocol to perform in vitro disease modeling of brachyolmia and metatropic dysplasia resulting from mutations in mechanosensor TRPV4. To enhance chondrogenesis, we used a CRISPR-Cas9-edited hiPSC cell line with a GFP reporter to determine surface markers co-expressed with early chondrogenic marker and cartilage matrix protein COL2A1. We found that chondroprogenitors that were positive for PDGFRβ, CD146, and CD166 and negative for CD45 had enhanced chondrogenic potential. In fact, sorted chondroprogenitors from the reporter line and an unedited line had significantly improved homogeneity compared to unsorted as determined by single-cell RNA sequencing. Furthermore, the derived chondrocytes synthesized more homogenous and robust matrix proteins and had higher chondrogenic gene expression. In a continued effort to improve the chondrogenesis protocol, we used bulk and single-cell RNA sequencing to determine where the off-target differentiation occurred. We found that Wnt and melanocyte inducing transcription factor (MITF) signaling were driving the two primary off-target populations: neurogenic and melanogenic, respectively. Single-cell RNA sequencing, histology, and quantification of matrix production confirmed pan-Wnt and MITF inhibition during chondrogenesis improved homogeneity of the cells throughout differentiation and increased chondrogenic potential. Using the findings from these studies, we created an hiPSC chondrogenesis protocol that follows the developmental mesodermal lineage and uses chemically defined medium. We also provide instructions for digesting the chondrogenic tissue to isolate hiPSC-derived chondrocytes at the single cell level. This protocol has applications for a variety of tissue engineering uses including regenerative therapies, gene editing, drug screening, and disease modeling. In fact, we applied this protocol for disease modeling of TRPV4 mutations that result in skeletal dysplasias. Using CRISPR-Cas9 gene editing technology, we created two hiPSC lines harboring either the brachyolmia-causing V620I substitution or the metatropic dysplasia-causing T89I substitution. The hiPSCs were chondrogenically differentiated and then were treated with BMP4 to stimulate hypertrophic differentiation. We determined that TRPV4 mutations increased basal signaling but decreased sensitivity to chemical agonist GSK1016790A using electrophysiology techniques and confocal imaging. Furthermore, using bulk RNA sequencing, we found the mutations suppressed chondrocyte maturation and hypertrophy, likely preventing endochondral ossification and long bone formation leading to the disease phenotype. We also used these cell lines to study the effects of the mutations on mechanotransduction. The hiPSC-derived chondrocytes were physiologically loaded in agarose constructs for 3 hours and then sequenced to elucidate the temporal response to loading. We found the mutant TRPV4 increased gene expression in response to loading compared to wildtype. Gene expression patterns indicated increased proliferation in mutant cells, which could prevent chondrocyte hypertrophic differentiation and endochondral ossification. Overall, we have developed an improved chondrogenic hiPSC protocol. The resulting tissue-engineered cartilage has many uses including in vitro disease modeling of genetic, developmental conditions, as shown here. Our findings provide target genes for future drug development to treat brachyolmia and metatropic dysplasia. Furthermore, we have increased the understanding of TRPV4 function in chondrocytes, which can be applied to cartilage tissue engineering and other cartilage disease studies

Principal Component Clustering Analysis Apply to the Amino Acid Content in Antler Based on Matlab

International audienceThe antler is a very high nutritional value of supplements, but also a very important medicine, antler occupies a very important position in Chinese medicine. Antler is rich in amino acids, which contains more than seven kinds of essential amino acids. In the organic component of antler, amino acids are the topped content of nutrients, and in them, the highest is glycine; amino acids are the basic components of living organism tissue cells, and play a pivotal role for life events. If the body lacks any kind of essential amino acids, it can cause physiological dysfunction, affecting the normal antibody metabolism, leading to disease. In the paper, it accord to the data of amino acids which contain different specifications antler herbs, analyze and compare the relationship of amino acids between sika deer antler and red deer antler using the principal component cluster analysis. The results showed that sika deer antler with red deer antler have the similar medicine effect and different essential amino acids nutrients

Novel Analytical Methods in Food Analysis

This reprint provides information on the novel analytical methods used to address challenges occurring at academic, regulatory, and commercial level. All topics covered include information on the basic principles, procedures, advantages, limitations, and applications. Integration of biological reagents, (nano)materials, technologies, and physical principles (spectroscopy and spectrometry) are discussed. This reprint is ideal for professionals of the food industry, regulatory bodies, as well as researchers