Fluorescence sensor array for identification of commercial milk samples according to their thermal treatments

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage

The application of spectroscopic techniques can help in alleviating problems encountered during the processing of milk and dairy products. Indeed, traditional analytical methods (e.g., physicochemical measurements, sensory, chromatography) are relatively expensive, time-consuming, and require chemicals and sophisticated analytical equipment, and skilled operators. Hence, there is a need to develop faster and less costly methods for accurately monitoring changes in the quality of milk and other dairy products during processing and storage.Many nondestructive and noninvasive instrumental techniques are available for inline and online monitoring of food. These include fluorescence spectroscopy, mid-infrared (MIR), near-infrared (NIR), nuclear magnetic resonance (NMR), etc. These techniques are usually used in combination with chemometric tools a to explore the information present in spectral data.This review article will discuss the potential of the above-mentioned spectroscopic techniques for monitoring chemical modifications of dairy products and the prediction of their functional properties during processing. The advantages and disadvantages of each technique are also discussed in this review. Finally, some conclusions are drawn, and the future trends of these methods are presented.

Identification of milk adulteration by a sensor array based on cationic polymer induced aggregation of a perylene probe

A novel fluorescence sensor array based on cationic polymer induced self-assembly of a perylene probe is developed. Cationic polymer induced aggregation of the carboxyl modified negatively charged perylene probe, and resulted in large quenching of monomer emission and generation of excimer emission. Upon the addition of negatively charged protein, monomer fluorescence restored with a decrease in excimer fluorescence. Based on these observations, we developed a six-channel sensor array to discriminate five main proteins in milk. In addition, we successfully identified pure milk out of different drinks using the developed sensor array since different drinks contained distinct species and contents of proteins. Furthermore, the sensor array exhibited excellent performance to discriminate milk adulterated by different concentrations of adulterants with 100% accuracy of cross validation. The analysis results also presented excellent linear correlation of adulterants contents and thus the developed sensor array shows great potential for quantitative detection of milk adulteration.

Spectroscopic Techniques for Monitoring Thermal Treatments in Fish and Other Seafood: A Review of Recent Developments and Applications

Cooking is an important processing method, that has been used since ancient times in order to both ensure microbiological safety and give desired organoleptic properties to the cooked food. Fish and other seafood products are highly sensitive to thermal treatments and the application of severe heat can result in negative consequences on sensory and nutritional parameters, as well as other quality attributes of the thermally processed products. To avoid such undesired effects and to extend the shelf life of these perishable products, both the heat processing methods and the assessment techniques used to monitor the process should be optimized. In this review paper, the most common cooking methods and some innovative ones will first be presented with a brief discussion of their impact on seafood quality. The main methods used for monitoring heat treatments will then be reviewed with a special focus on spectroscopic techniques, which are known to be rapid and non-destructive methods compared to traditional approaches. Finally, viewpoints of the current challenges will be discussed and possible directions for future applications and research will be suggested. The literature presented in this review clearly demonstrates the potential of spectroscopic techniques, coupled with chemometric tools, for online monitoring of heat-induced changes resulting from the application of thermal treatments of seafood. The use of fluorescence hyperspectral imaging is especially promising, as the technique combines the merits of both fluorescence spectroscopy (high sensitivity and selectivity) and hyperspectral imaging (spatial dimension). With further research and investigation, the few current limitations of monitoring thermal treatments by spectroscopy can be addressed, thus enabling the use of spectroscopic techniques as a routine tool in the seafood industry.

Surfactant and alcohol induced disaggregation of perylene probes and a novel sensing strategy for distinguishing the brand and authenticity of makeup removers

A novel sensing strategy based on four perylene probes to distinguish the brand and authenticity of makeup removers.

The combined use of 1 H and 2D NMR-based metabolomics and chemometrics for non-targeted screening of biomarkers and identification of reconstituted milk

BACKGROUND

The illegal undeclared addition of reconstituted milk powder to ultra-heat treated (UHT) milk to lower production costs is an example of economically motivated adulteration. This activity not only defrauds consumers but also places honest traders at a disadvantage, which could damage the reputation of milk producers and reduce the integrity of the markets. In this research, a non-targeted analytical strategy that combines proton (¹ H) nuclear magnetic resonance (NMR) spectroscopy with a chemometrics data mining tool was developed for the authentication of bovine UHT milk.

RESULTS

Unsupervised principal component analysis was used to distinguish UHT and tap-water-reconstituted powdered milk. Partial least squares-discriminant analysis (PLS-DA) with R² (Y) and Q² equal to 0.859 and 0.748, respectively, was used to differentiate UHT and reconstituted milk samples. Three compounds were selected as biomarkers to distinguish UHT and reconstituted milk and identified according to the standard NMR-spectra database. Finally, a PLS-DA model was established, according to the characteristic spectral bands, to identify UHT milk and reconstituted milk.

CONCLUSION

This procedure demonstrated the feasibility of using non-targeted NMR profiling combined with chemometric analysis to combat mislabeling and fraudulent practices in milk production. © 2019 Society of Chemical Industry.

Plant Biomarker Recognition by Molecular Imprinting Based Localized Surface Plasmon Resonance Sensor Array: Performance Improvement by Enhanced Hotspot of Au Nanostructure

Detection of plant volatile organic compounds (VOCs) enables monitoring of pests and diseases in agriculture. We previously revealed that a localized surface plasmon resonance (LSPR) sensor coated with a molecularly imprinted sol-gel (MISG) can be used for cis-jasmone vapor detection. Although the selectivity of the LSPR sensor was enhanced by the MISG coating, its sensitivity was decreased. Here, gold nanoparticles (AuNPs) were doped in the MISG to enhance the sensitivity of the LSPR sensor through hot spot generation. The size and amount of AuNPs added to the MISG were investigated and optimized. The sensor coated with the MISG containing 20 μL of 30 nm AuNPs exhibited higher sensitivity than that of the sensors coated with other films. Furthermore, an optical multichannel sensor platform containing different channels that were bare and coated with four types of MISGs was developed to detect plant VOCs in single and binary mixtures. Linear discriminant analysis, k-nearest neighbor (KNN), and naïve Bayes classifier approaches were used to establish plant VOC identification models. The results indicated that the KNN model had good potential to identify plant VOCs quickly and efficiently (96.03%). This study demonstrated that an LSPR sensor array coated with a AuNP-embedded MISG combined with a pattern recognition approach can be used for plant VOC detection and identification. This research is expected to provide useful technologies for agricultural applications.

A Fluorescent Sensor Array Based on Heteroatomic Macrocyclic Fluorophores for the Detection of Polluting Species in Natural Water Samples

The development of a novel all-solid-state optical sensor array based on heteroatomic macrocyclic fluorophores (diaza-crown ether, metallocorrole and pyridinophans) for the photographic analysis of liquid media, is presented. The sensitivity of the new optical system toward a number of different species (cations: Li⁺, K⁺, Na⁺, NH4+, Mg²⁺, Ca²⁺, Co²⁺, Cu²⁺, Zn²⁺, Cd²⁺, Pb²⁺ and anions: NO2-, NO3-, Cl⁻, Br⁻, HCO3-) was evaluated both in single selective sensor mode and in multisensory arrangement. The satisfactory PLS1 regression models between sensor array optical response and analyte concentration were obtained for Cd²⁺, Cu²⁺, Zn²⁺, and NO2- ions in all the range of tested concentrations. Among these species the highest attention was focused onto detection of cadmium and nitrite ions, for which the detection limits, DL, estimated by 3σ method were found 0.0013 mg/L and 0.21 mg/L respectively, and these values are lower than the corresponding WHO guideline values of 0.003 mg/L (Cd²⁺) and 2 mg/L (NO2-). The suitability of the developed sensors implemented with familiar devices for signal acquisition (Light Emitting Diode, LED, as light source and a digital camera as a signal detector), and chemometric methods for data treatment to perform fast and low-cost monitoring of species under interest, in real samples of environmental importance, is demonstrated.

Fluorescence Spectroscopy for the Monitoring of Food Processes

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.

New Trends for the Evaluation of Heat Treatments of Milk

Milk is generally very rich in nutrients and this may lead it to be an ideal growth environment for many microorganisms, including pathogens, so effective measurements aiming to ensure total microbiological safety of milk and minimize the risk to human health are needed. Milk heat treatments are the most common practices carried out to inhibit the microbial growth; therefore it is necessary to have analytical procedures that are more and more up-to-date and capable of detecting the effectiveness of the heat treatments. Most of the reference and official methods to assess heat treatment in milk are based on the evaluation of the modifications of some milk components following the thermal process, such as the determination of enzyme activities (alkaline phosphatase and lactoperoxidase), whey proteins, Maillard reaction compounds (generally furosine), and lactulose. Besides the most common techniques (liquid and gas chromatography, capillary electrophoresis, or spectroscopy) used for the detection of single thermal indicators, new approaches, such as chemometric studies or more recent techniques, including size-exclusion chromatography with online electrospray mass spectrometry or stable isotope ratio mass spectrometry, are discussed in this review in order to evaluate heat treatment in milk.

Turn-on fluorescent sensor for the detection of cyanide based on a novel dicyanovinyl phenylacetylene

A novel phenylacetylene derivative (3) was successfully synthesized via Sonogashira coupling and a Knoevenagel reaction for cyanide ion detection.