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Abstract: This research explored the use of spatially offset Raman spectroscopy (SORS) for nondestructive evaluation of internal maturity of tomatoes. A Raman system using a 785-nm laser was developed to collect spatially offset spectra in the wavenumber range of 200–2500cm−1. The SORS measurements were conducted using a source-detector distance ranging from 0 to 5mm with a step size of 0.2mm. One hundred and sixty tomatoes at seven ripeness stages (i.e., immature green, mature green, breaker, turning, pink, light red, and red) were tested. The feasibility of the SORS for subsurface detection was examined by using a Teflon slab placed under outer pericarp slices of 5-mm and 10-mm thicknesses cut from green and red tomatoes. Raman signals from the outer pericarp layer and the Teflon layer were effectively separated by self-modeling mixture analysis of the offset spectra after fluorescence correction. Three Raman peaks due to carotenoids inside the tomatoes started showing at the mature green stage. Two peaks appeared consistently at 1001 and 1151cm−1, and the third peak was gradually shifted from 1525cm−1 (lutein at mature green stage) to 1513cm−1 (lycopene at red stage) owing to the loss of lutein and β-carotene and the accumulation of lycopene during tomato ripening. The Raman peak changes were evaluated by spectral information divergence (SID) with pure lycopene as the reference. The SID values decreased as the tomatoes ripened, and thus these values can be used to evaluate the internal ripeness of tomatoes. [Copyright &y& Elsevier]

Packaged food safety has gained increasing attention worldwide. Existing analytical methods pose difficulties in accurately measuring food quality without destroying the packaging. In this study, a nondestructive detection method for packaged food was proposed based on the generalized Gaussian model for Raman scattering images. The Raman peaks of the scattering image were extracted, and the attenuation information of the peaks far from the laser point were imported into the established generalized Gaussian model. Analysis of the histogram of residual distribution revealed that the difference in residual distribution was enhanced, and an appropriate threshold was selected to separate the Raman baseline correction spectrum of the internal materials. Food-grade polyethylene sheets with thicknesses of 1, 2, and 3 mm were used as packaging materials for comparison experiments. The proposed model can accurately separate the Raman peak of the subsurface material when 1 mm-thick polyethylene was used as the packaging. Food-grade plastic sheets of polyethylene, polypropylene and high-density polyethylene were covered with pure substances such as melamine, sodium nitrite, and maleic anhydride. This model was considered suitable for most food-grade plastic packaging, and the subsurface materials did not influence the separation effect. Finally, evaluation of premium white granulated sugar demonstrated that the model effectively separated the Raman peak produced by packaged food and detected the packaged food without conferring damage. • This method can be applied to nondestructive testing with packaged foods. • Generalized Gaussian model separated food and package Raman peaks. • Two-layer sample experiments verified that this method is effective. • Analyzed the scope of application by changing the sample materials. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improved Monte Carlo simulates spatially offset Raman spectroscopy of packaged foods. • Evaluated the signal separation effect of MCR-ALS and FastICA on simulated SORS data. • This method can be applied to nondestructive testing with packaged foods. • Verified the effectiveness of FastICA by three commercial packaged foods. Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food. [ABSTRACT FROM AUTHOR]

[Display omitted] • This method can be applied to nondestructive testing with packaged foods. • Mathematical model analysis of spatially offset Raman spectroscopy technique. • Improved FastICA separated food Raman signal with packaging. • Verified the effectiveness of this method by multi-sample experiments. Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food. [ABSTRACT FROM AUTHOR]

Highlights: [•] Optical instrument was developed for pesticide residue detection in apple surface. [•] Hardware and software system was self developed as prototype instrument. [•] The instrument can detect chlorpyrifos pesticide in apple surface nondestructively. [•] 6.69mg/kg of pesticide residue in apple surface is detectable by the instrument. [•] The system can detect the pesticide residue in apple surface within 4s. [ABSTRACT FROM AUTHOR]

Prawns are highly popular with consumers but present many technical difficulties for the evaluation of their internal quality when intact (in-shell prawns). This study proposed a nondestructive method to assess the internal quality of intact prawns (Fenneropenaeus chinensis) using spatially offset Raman spectroscopy (SORS) technique combined with data modeling analysis. This technique holds promise due to the capability of SORS to obtain chemical information nondestructively from below the surface of a sample material. Raman scattering image data for 100 fresh prawns (approximately 15 g each) were collected using a line-scan Raman imaging system over the course of seven days with 24 h measurement intervals. Measurement anomalies due to physical prawn irregularities were eliminated using a peak identification method. Twenty feature bands selected by Random Forest (RF) method were input to Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extremely Randomized Tree (ET) models to predict the freshness of prawns during the storage time. The prediction model based on SORS enhanced data and combining RF feature band selection with SVR demonstrated the best performance, with RMSEP, R2, and RPD values of 0.71, 0.88, and 2.63, respectively. This rapid and nondestructive method for quality evaluation may be feasible as a practical means of assessing internal quality of materials that demonstrate surface interference, such as in-shell prawns. • This method can be applied to nondestructive testing with intact prawns. • Spatially offset Raman spectroscopy technique enhanced internal signal. • Feature bands were extracted by Random Forest model. • Verified the effectiveness of this method by multi-model comparison. [ABSTRACT FROM AUTHOR]