Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Method of Optical Diagnostics of Grain Seeds Infected with Fusarium

Optical sensors have shown good capabilities for detecting and monitoring plant diseases, including fusariosis. The spectral characteristics of the excitation and luminescence of wheat, oat and barley seeds were measured using a diffraction spectrofluorimeter in the range of 180–700 nm. It was found that during infection, the spectral density of the absorption capacity increases and the curve ηe(λ) shifts upwards in the range of 380–450 nm. The shift to the left is also noticeable for the wheat and barley spectra. The photoluminescence flux at λe = 232 nm increased by 1.71 times when oat seeds were infected, by 2.63 times when wheat was infected and by 3.14 times when barley was infected. The dependences of the infection degree on the photoluminescence flux are statistically and reliably approximated by linear regression models with determination coefficients R2 = 0.83–0.95. The method of determining the degree of infection can include both absolute measurements of photoluminescence flux in the range of 290–380 nm and measurements of the flux ratios when excited by radiation of 232 nm and 424 nm for wheat and 485 nm for barley. An optoelectronic device for remote monitoring can be designed in order to implement the methodology for determining the degree of infection of agricultural plant seeds.

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Novel Nondestructive Biosensors for the Food Industry

An increasing number of foodborne outbreaks, growing consumer desire for healthier products, and surging numbers of food allergy cases necessitate strict handling and screening of foods at every step of the food supply chain. Current standard procedures for detecting food toxins, contaminants, allergens, and pathogens require costly analytical devices, skilled technicians, and long sample preparation times. These challenges can be overcome with the use of biosensors because they provide accurate, rapid, selective, qualitative, and quantitative detection of analytes. Their ease of use, low-cost production, portability, and nondestructive measurement techniques also enable on-site detection of analytes. For this reason, biosensors find many applications in food safety and quality assessments. The detection mechanisms of biosensors can be varied with the use of different transducers, such as optical, electrochemical, or mechanical. These options provide a more appropriate selection of the biosensors for the intended use. In this review, recent studies focusing on the fabrication of biosensors for food safety or food quality purposes are summarized. To differentiate the detection mechanisms, the review is divided into sections based on the transducer type used.