Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models

For predicting the shelf life of processed cheese stored at 7-8 C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese

Artificial Intelligence : Implications for the Agri-Food Sector

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized

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

Data mining and cluster organisations : the case of PortugalFoods

Even though the concept of clusters received a considerable amount of attention, the literature dedicated to cluster organisations is still very scarce. On the other hand, the widely applicability of data mining to several industries, along with the benefits that it might bring to any organisation, have been the subject of various articles throughout the years. This dissertation intends to assess how could cluster organisations benefit from the application of data mining on the type of services they provide. Through the empirical study of a Portuguese cluster organisation – PortugalFoods – I analysed if data mining represents an opportunity for these governance bodies, particularly if applied as a new support tool on their market intelligence services. Supported by CRISP-DM methodology, and based on data provided by Mintel’s databases, a prototype data mining project was developed. The findings of this study indicate that data mining could enhance PortugalFoods’ market intelligence services, as well as their role as producers and disseminators of knowledge. Yet, challenges were also detected, due to the existence of several data’s problems, which could jeopardize the future replication of this process

7th International ISEKI-Food Conference: next-generation of food research, education and industry. Book of abstracts

As part of its mission, ISEKI-Food Association establishes and maintains a network among universities, research institutions, and companies in the food chain in addition to working to ensure that food studies are of high quality. However, we must also begin planning how to gear science, education, and the food industry to meet the needs of future generations as well as how to contribute to the sustainability of our planet by these food actors. In light of this, the 7th International ISEKI-Food Conference, which had as main theme “NEXT-GENERATION OF FOOD RESEARCH, EDUCATION AND INDUSTRY”, focused on future challenges in education on food science and technology, in research activities related to processing, quality and safety, packaging of foods and in societal engagements in the field divided in three main sections: EDUCATION: CHALLENGES OF EDUCATION IN A CHANGING WORLD; RESEARCH: NEXT GENERATION OF FOODS; and SOCIETY ENGAGEMENT: SOCIETY AND FOOD INDUSTRY. The conference was dedicated to all food actors, creating bridges among them. The delegates had the opportunity to exchange new ideas and experiences face to face, to establish business or research relations, and find global partners for future collaborations.info:eu-repo/semantics/publishedVersio

Development of an intelligent analytics-based model for product sales optimisation in retail enterprises

A retail enterprise is a business organisation that sells goods or services directly to consumers for personal use. Retail enterprises such as supermarkets enable customers to go around the shop picking items from the shelves and placing them into their baskets. The basket of each customer is captured into transactional systems. In this research study, retail enterprises were classified into two main categories: centralised and distributed retail enterprises. A distributed retail enterprise is one that issues the decision rights to the branches or groups nearest to the data collection, while in centralised retail enterprises the decision rights of the branches are concentrated in a single authority. It is difficult for retail enterprises to ascertain customer preferences by merely observing transactions. This has led to quantifiable losses. Although some enterprises implemented classical business models to address these challenging issues, they still lacked analytics-based marketing programs to gain competitive advantage. This research study develops an intelligent analytics-based (ARANN) model for both distributed and centralised retail enterprises in the cross-demographics of a developing country. The ARANN model is built on association rules (AR), complemented by artificial neural networks (ANN) to strengthen the results of these two individual models. The ARANN model was tested using real-life and publicly available transactional datasets for the generation of product arrangement sets. In centralised retail enterprises, the data from different branches was integrated and pre-processed to remove data impurities. The cleaned data was then fed into the ARANN model. On the other hand, in distributed retail enterprises data was collected branch per branch and cleaned. The cleaned data was fed into the ARANN model. According to experimental analytics, the ARANN model can generate improved product arrangement sets, thereby improving the confidence of retail enterprise decision-makers in competitive environments. It was also observed that the ARANN model performed faster in distributed than in centralised retail enterprises. This research is beneficial for sustainable businesses and consideration of the results is therefore recommended to retail enterprises.ComputingM Sc. (Computing

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program 1988, volume 1

The 1988 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston and JSC. The 10-week program was operated under the auspices of the ASEE. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington, D.C. The objectives of the program, which began in 1965 at JSC and in 1964 nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objectives of the NASA Centers

Exploratory analysis of Internet of Things (IoT): revolutionizing the grocery retail industry

This dissertation has investigated the consequences of implementing Internet of Things (IoT) technologies in grocery retailing by analyzing customers' perceptions of eight prominent technologies. The objective was to investigate and explore to what degree implementing these technologies would impact the customer experience. Based on secondary research, this thesis focuses on eight prominent technologies that presumably will encounter an increasing utilization in the visible future; Self-Scanning, Smart Robots, Smart Shelves, Smart Shopping Cart, Smart Fridge, Just Walk Out, Personalized Promotion/Pricing, and Mobile Apps. The technology distribution varies across different stages in the customer journey, and research indicates that IoT has the most significant impact in the pre-purchase stage. A comprehensive exploratory survey was conducted through Amazon mTurk with a wide range of respondents (n=204), giving valuable insight into demographic differences' influence on each technology perception. The investigation uncovered vast differences in several areas such as age, attitude, and privacy. Among other findings, the age segment 35-44 is more confident towards IoT technology than the age segment 55+, and shoppers with a positive attitude towards grocery shopping have higher confidence towards the technologies than shoppers with a negative attitude. On a widespread basis, the findings revealed that all eight technologies would positively affect customer experience to a certain level. Keywords: Internet of Things, Grocery Retailing, Customer Journey, Customer Experience, Autonomous Retail