Detection of doping of avocado oil using Fourier near-infrared spectroscopy
2024-02-21 05:07:14
Several analytical methods for detecting the doping of avocado oil have been developed on the market. Many analytical methods rely on chromatographic techniques, but such methods can take a long time to prepare samples and can produce harmful chemical waste. 3 In contrast, Near Infrared Spectroscopy and Adulterant ScreenTM technology can quickly detect the doping of avocado oil without the need for solvents.
Currently, targeted dopant screening methods using near-infrared spectroscopy require the establishment of relevant quantitative calibration models based on various potential dopants. In addition, non-targeted screening methods such as the SIMCA (Soft Independent Modeling Taxonomy) algorithm can determine if a sample is doped, but neither the dopant nor the dopant can be quantified. On the other hand, PerkinElmer's dopant screening algorithm provides a semi-targeted screening method that can quickly identify and estimate doping.
experiment
Near-infrared spectra of pure shea butter and four possible doping oils were collected using a PerkinElmer Spectrum Two NTM Fourier Near Infrared Spectrometer equipped with a PerkinElmer Heated Transmission Module ( HTM ) . The temperature is controlled at 25 °C .
The sample was placed in an 8 mm glass vial to achieve thermal equilibrium in the heatable transmissive module and scanned using the parameters shown in Table 1 . Collected pure shea butter (three different brands of commercially available 5 replicates) 15 spectra, and each dopant (peanut oil, olive oil, rapeseed oil and sunflower oil) is mixed for a spectrum Weed screening method. The spectra were pretreated, the spectral range was adjusted to 10,000-4500 cm-1 , the area with absorption higher than 1.5 was eliminated , and the first derivative baseline was used for calibration, as shown in Figure 2 .
In addition, each dopant was added to 16 parts of pure shea butter sample to obtain a doped sample having a doping concentration ranging from 2 to 95% (w/w) . The spectra of each doped sample, pure avocado oil sample, and purely doped oil sample can be used to establish a partial least squares ( PLS1 ) quantitative model of each dope by a PerkinElmer Spectrum QuantTM analyzer . Fifteen samples were used for calibration and three samples ( 25% , 55%, and 85%, respectively ) were used for independent validation of the model. In addition, each model was also cross-validated. The spectra in each model were pretreated using the parameters shown in Table 2 .
in conclusion
The test results show that the PerkinElmer near-infrared spectroscopy analyzer with a heatable transmission module (HTM) can accurately detect and identify the doping oil in avocado oil. The PLS1 calibration model all accurately predicts the current doping. However, this method requires a long time to prepare and measure a calibration standard sample. On the other hand, the dopant screening algorithm quickly identifies the presence of dopant species and estimates the dopant concentration relatively accurately. If the dopant screening result of a sample is unacceptable, the sample may be further tested later. If a new dopant is present, then simply add a spectrogram of the pure new dopant to the pure dopant spectrum library. Therefore, dopant screening is a more appropriate method for routine inspection of avocado oil doping.
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