Chapter 15 - Food Authenticity and Fraud

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  • Chemi-resistive sensing of methylamine species using twinned α-MoO<inf>3</inf> nanorods: Role of grain features, activation energy and surface defects

    2021, Sensors and Actuators B: Chemical
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    The concentration of TMA signifying the fresh condition of fish samples is below 10 ppm, and a concentration greater than 60 ppm is graded as spoiled condition [2,8–10]. The existing instrumental techniques [11–14] and chemical analysis [9,15] for the detection of methylamines are typically invasive, require complex sample preparation, and are laborious [16]. Thus, the demand for trace level detection of methylamine species in a non-invasive manner has been on the rise [17].

  • Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

    2020, Analytica Chimica Acta
    Citation Excerpt :

    As non-target HRMS methodologies result in the generation and detection of a large number of markers (m/z), their identification constitutes a challenging task. The coupling of HRMS with chemometric tools could decrease remarkably the number of the detected features and lead to the most meaningful m/z marker in the investigation of crucial EVOOs/VOOs authenticity issues [72]. The next section summarizes the most recent application of advanced chemometric tools coupled to HRMS screening strategies in aforementioned EVOOs/VOOs authenticity issues.

  • Development of electronic nose (Shrimp-Nose) for the determination of perishable quality and shelf-life of cultured Pacific white shrimp (Litopenaeus Vannamei)

    2020, Sensors and Actuators, B: Chemical
    Citation Excerpt :

    Perishable quality and shelf life of Pacific white shrimps can be related with few parameters such as textural (hardness, springiness, gumminess, and chewiness) [4], melanosis (color and appearance) [5,6], chemical (pH, functional groups and TVBN) [7,8], microbiological (total plate count, TPC)) [9] and sensory changes [7,10]. In general, quality changes can be observed through conventional techniques such as High-Performance Liquid Chromatography (HPLC) [11,12], Impedance Spectroscopy [13], Nuclear Magnetic Resonance (NMR) Spectroscopy [12,14,15], UV–vis spectrophotometer [16,17], Hyperspectral Imaging [18,19], Magnetic Resonance Imaging (MRI) [20], Ultrasound Imaging (UI) [21], Thermal Imaging (TI) [22] and Enzyme-linked immunosorbent assays (ELISAs) [23,24]. However, these methods require complex sample preparation, need skilled personnel and frequent calibration, and are expensive [13].

  • Electronic noses and tongues to assess food authenticity and adulteration

    2016, Trends in Food Science and Technology
    Citation Excerpt :

    After briefly commenting the fundamentals of this type of devices, the most relevant contributions in this field over the past ten years will be dealt with. In this sense, and as a general overview, in a recent chapter of a book (Karoui, 2012) devoted to food authenticity and fraud, Karoui discusses the relative potential and ease of application of different technologies for the confirmation of food quality and adulteration. Special emphasis is put on e-nose technology (combined with chemometric tools) as a promising technique in this field.

  • The current status of process analytical technologies in the dairy industry

    2015, Trends in Food Science and Technology
    Citation Excerpt :

    Miquel Becker et al. (2003) used FFFS coupled with chemometrics to monitor light-induced changes (i.e. riboflavin content monitoring) in yogurt during storage. FFFS combined with chemometrics has also been used for the authentication of dairy products, the detection of adulteration and to control fraudulent practices (Karoui, 2012). Electronic Noses (EN) and Electronic Tongues (ET) are low-cost spectroscopic techniques employing chemical sensors similar to mammalian senses that produce a unique composite response to each odorant (Yu et al. 2007; Dias et al. 2009; Wang et al. 2010; Haddi et al. 2011; Wei, Wang, & Jin, 2013).

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