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[HTML][HTML] Deep learning for Raman spectroscopy: A review
Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the
vibrational states within samples. This information on vibrational states can be utilized as …
vibrational states within samples. This information on vibrational states can be utilized as …
Raman microspectroscopy for microbiology
Raman microspectroscopy offers microbiologists a rapid and non-destructive technique to
assess the chemical composition of individual live microorganisms in near real time. In this …
assess the chemical composition of individual live microorganisms in near real time. In this …
Recent advances in shelf life prediction models for monitoring food quality
F Cui, S Zheng, D Wang, X Tan, Q Li… - … Reviews in Food …, 2023 - Wiley Online Library
Abstract Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain,
accounting for approximately one third of global food production. This requires a …
accounting for approximately one third of global food production. This requires a …
Applications of machine learning in spectroscopy
The way to analyze data in spectroscopy has changed substantially. At the same time, data
science has evolved to the point where spectroscopy can find space to be housed, adapted …
science has evolved to the point where spectroscopy can find space to be housed, adapted …
[HTML][HTML] Data augmentation techniques for machine learning applied to optical spectroscopy datasets in agrifood applications: A comprehensive review
Machine learning (ML) and deep learning (DL) have achieved great success in different
tasks. These include computer vision, image segmentation, natural language processing …
tasks. These include computer vision, image segmentation, natural language processing …
Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel
Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for
food industry. However, obtaining a large number of oil content reference values of maize …
food industry. However, obtaining a large number of oil content reference values of maize …
Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria
Y Du, D Han, S Liu, X Sun, B Ning, T Han, J Wang… - Talanta, 2022 - Elsevier
Raman spectroscopy combined with artificial intelligence algorithms have been widely
explored and focused on in recent years for food safety testing. It is still a challenge to …
explored and focused on in recent years for food safety testing. It is still a challenge to …
[HTML][HTML] In situ identification of environmental microorganisms with Raman spectroscopy
Microorganisms in natural environments are crucial in maintaining the material and energy
cycle and the ecological balance of the environment. However, it is challenging to delineate …
cycle and the ecological balance of the environment. However, it is challenging to delineate …
Real-time image-based air quality estimation by deep learning neural networks
Air quality profoundly impacts public health and environmental equity. Efficient and
inexpensive air quality monitoring instruments could be greatly beneficial for human health …
inexpensive air quality monitoring instruments could be greatly beneficial for human health …
Development of a new hyperspectral imaging technology with autoencoder-assisted generative adversarial network for predicting the content of polyunsaturated fatty …
The establishment of a comprehensive predictive model for red meat polyunsaturated fatty
acids holds profound significance for the food industry. However, challenges, such as …
acids holds profound significance for the food industry. However, challenges, such as …