Taotao Mu

Characterization of edible oils using time-resolved fluorescence

To analyze and detect edible oils, a spectral recognition method is introduced based on time-resolved fluorescence (TRF). Contour diagrams of TRF intensities superior to steady-state fluorescence are constructed to serve as unique fingerprints for edible oils, which can be used to identify vegetable oils. The proposed approach provides a rapid and reliable means for analyzing and classifying edible oils in food security research fields. Reliability and stability are verified by TRF measurements of peanut oils of three brands in a supplementation trial.

Classification of edible oils using 532 nm laser-induced fluorescence combined with support vector machine

In this paper, laser-induced fluorescence (LIF) is used to characterize and distinguish between different vegetable oils, including soybean, olive, grapeseed, rapeseed, corn, peanut, sunflower, canola, and walnut oils. A 532 nm laser, rather than an ultraviolet (UV) light source, is proposed and used as an excitation light source for the fluorescence analysis of edible oils. It was found that this laser is superior to UV lasers, the fluorescent characteristics become more distinct under 532 nm laser excitation. Edible oils were differentiated by LIF combined with principal component analysis which was used to reduce the dimensionality of data by finding key attributes, and support vector machine. This paper demonstrates, that for ten popular edible oils, the recognition rate can reach up to 100% when a 532 nm laser serves as an excitation light source.

Motor Oil Classification Based on Time-Resolved Fluorescence

A time-resolved fluorescence (TRF) technique is presented for classifying motor oils. The system is constructed with a third harmonic Nd:YAG laser, a spectrometer, and an intensified charge coupled device (ICCD) camera. Steady-state and time-resolved fluorescence (TRF) measurements are reported for several motor oils. It is found that steady-state fluorescence is insufficient to distinguish the motor oil samples. Then contour diagrams of TRF intensities (CDTRFIs) are acquired to serve as unique fingerprints to identify motor oils by using the distinct TRF of motor oils. CDTRFIs are preferable to steady-state fluorescence spectra for classifying different motor oils, making CDTRFIs a particularly choice for the development of fluorescence-based methods for the discrimination and characterization of motor oils. The two-dimensional fluorescence contour diagrams contain more information, not only the changing shapes of the LIF spectra but also the relative intensity. The results indicate that motor oils can be differentiated based on the new proposed method, which provides reliable methods for analyzing and classifying motor oils.

Determining the orientation of transition moments and depolarization by fluorescence polarizing angle spectrum

In this paper, fluorescence polarizing angle spectrum, combined with degree of polarization(DOP), is proposed to determine the spatial orientation of transition dipole moments (TDMs) and depolarization of chlorophyll in solution. It is found that, due to the oriented TDMs under polarized laser excitation, the projections of angle of polarization(AOP) and DOP on the three orthogonal planes are different from each other. Experiments demonstrate that we can acquire the spatial orientation by detecting the projections of AOP on two orthogonal planes (xOz and yOz). Meanwhile, The depolarization can also be determined by the DOP spectrums. The validity of this method has been verified by another projection on the xOy plane.

Fluorescence polarization technique: a new method for vegetable oils classification

Concern about classification of different edible oils has risen recently in food safety. Developing an effective oil classification method is essential for public health. In this paper, a novel fluorescence polarization technique is developed to classify various types of oils. The degree of polarization (DOP) spectra of seven vegetable oils are collected and analyzed. Classification of olive oil, walnut oil, and other types of oils is successfully achieved with DOP spectra under 532 nm laser excitation. A classification accuracy of 100% is obtained for the investigated samples. The method is non-destructive and requires no sample preparation, which provides insight to develop a novel portable monitor system concerning food safety.

Analyzing fluorophore electronic structure and depolarization by fluorescence polarizing angle spectrum

In this Letter, a method, based on stokes parameters, is developed to observe the angular displacement between the excitation and emission moments. Experiments demonstrate that when combined with degree of polarization spectrums, we can acquire the depolarization caused by angular displacement or energy migration. The method presented in this Letter can be easily realized with the existing fluorescence measuring system and may potentially make it convenient to study the fluorophore electronic structure or the mechanism of fluorescence anisotropy.

Characterization of Motor Oil by Laser-Induced Fluorescence

A new laser-induced fluorescence method is presented to classify brands of motor oil. A 266-nanometer radiation was employed to collect sixty sets of fluorescence spectra for each brand. Cluster analysis and support vector machine were employed to distinguish motor oils with principal component analysis, which was applied to extract spectral characteristics. The recognition accuracy was up to 100 percent. The results demonstrate that laser-induced fluorescence offers rapid response, high recognition, and high sensitivity for the classification of motor oils.

Portable detection system of vegetable oils based on laser induced fluorescence

Food safety, especially edible oils, has attracted more and more attention recently. Many methods and instruments have emerged to detect the edible oils, which include oils classification and adulteration. It is well known than the adulteration is based on classification. Then, in this paper, a portable detection system, based on laser induced fluorescence, is proposed and designed to classify the various edible oils, including (olive, rapeseed, walnut, peanut, linseed, sunflower, corn oils). 532 nm laser modules are used in this equipment. Then, all the components are assembled into a module (100*100*25mm). A total of 700 sets of fluorescence data (100 sets of each type oil) are collected. In order to classify different edible oils, principle components analysis and support vector machine have been employed in the data analysis. The training set consisted of 560 sets of data (80 sets of each oil) and the test set consisted of 140 sets of data (20 sets of each oil). The recognition rate is up to 99%, which demonstrates the reliability of this potable system. With nonintrusive and no sample preparation characteristic, the potable system can be effectively applied for food detection.

The double grating monochromator’s design for pure rotational Raman lidar

The pure rotational Raman lidar temperature measurement system is usually used for retrieval of atmospheric temperature according to the echo signal ratio of high and low-level quantum numbers of N2 molecules which are consistent with the exponential relationship. An effective method to detect the rotational Raman spectrum is taking a double grating monochromator. In this paper the detection principle and the structure of the dual-grating monochromator are described, with analysis of rotational Raman’s Stokes and anti-Stokes spectrums of N2 molecule, the high order and lower order quantum number of the probe spectrum are resolved, then the specific design parameters are presented. Subsequently spectral effect is simulated with Zemax software. The simulation result indicates that under the condition of the probe laser wavelength of 532nm and using double-grating spectrometer which is comprised by two blazed gratings, Raman spectrums of 529.05nm, 530.40nm, 533.77nm, 535.13nm can be separated well, and double-grating monochromator has high diffraction efficiency.

Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.