Comparison of Benchtop Fourier-Transform (FT) and Portable Grating Scanning Spectrometers for Determination of Total Soluble Solid Contents in Single Grape Berry (Vitis vinifera L.) and Calibration Transfer

Relationship between optical properties and internal quality of Orah Mandarins during storage

The optical properties [absorption coefficient (μa) and reduced scattering coefficient (μ's)] and internal qualities [moisture content (MC), and soluble solids content (SSC)] of Orah mandarin stored at 20 °C for 0-6 days were determined and and the relationship between the optical properties and different quality was explored. The SSC of Orah mandarin initially increased and then decreased, which was due to the phenomenon of low temperature saccharification. The MC remained relatively stable with a slight increase. The pulp tissue absorption coefficient (μa) has two absorption peaks at 500 nm and 980 nm, respectively associated with pigments and water. The exocarp tissue has an additional absorption peak at 500 nm related to color, while endocarp tissue only has one absorption peak at 980 nm related to water. The μa spectra showed significant correlations with MC and SSC. Partial Least Squares Regression (PLSR) models for predicting the SSC and MC of Orah mandarins were established based on μa and μ's spectra. The results showed that the prediction model based on μa spectra had the best performance. The Correlation Coefficient of Prediction (Rp) and Root Mean Square Error of Prediction (RMSEP) for SSC were 0.921 and 0.549, respectively; for MC, Rp and RMSEP were 0.906 and 0.636 respectively. These results indicate the potential of using the optical properties of Orah mandarins to predict internal quality.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

A Model Transfer Method among Spectrometers Based on Improved Deep Autoencoder for Concentration Determination of Heavy Metal Ions by UV-Vis Spectra

Ultraviolet Visible (UV-Vis) spectroscopy detection technology has been widely used in quantitative analysis for its advantages of rapid and non-destructive determination. However, the difference of optical hardware severely restricts the development of spectral technology. Model transfer is one of the effective methods to establish models on different instruments. Due to the high dimension and nonlinearity of spectral data, the existing methods cannot effectively extract the hidden differences in spectra of different spectrometers. Thus, based on the necessity of spectral calibration model transfer between the traditional large spectrometer and the micro-spectrometer, a novel model transfer method based on improved deep autoencoder is proposed to realize spectral reconstruction between different spectrometers. Firstly, two autoencoders are used to train the spectral data of the master and slave instrument, respectively. Then, the hidden variable constraint is added to enhance the feature representation of the autoencoder, which makes the two hidden variables equal. Combined with a Bayesian optimization algorithm for the objective function, the transfer accuracy coefficient is proposed to characterize the model transfer performance. The experimental results show that after model transfer, the spectrum of the slave spectrometer is basically coincident with the master spectrometer and the wavelength shift is eliminated. Compared with the two commonly used direct standardization (DS) and piecewise direct standardization (PDS) algorithms, the average transfer accuracy coefficient of the proposed method is improved by 45.11% and 22.38%, respectively, when there are nonlinear differences between different spectrometers.

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

From the Laboratory to The Vineyard-Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry

The quality of strawberry powder depends on the freshness of the fruit that produces the powder. Therefore, identifying whether the strawberry powder is made from freshly available, short-term stored, or long-term stored strawberries is important to provide consumers with quality-assured strawberry powder. Nevertheless, such identification is difficult by naked eyes, as the powder colours are very close. In this work, based on the measurement of near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectra of strawberry powered, good classification results of 100.00% correct rates to distinguish whether the strawberry powder was made from freshly available or stored fruit was obtained. Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. Optimal variables were selected by successive projections algorithm (SPA), uninformation variable elimination, and competitive adaptive reweighted sampling, respectively. The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. The results of this work confirmed the feasibility of using NIR and MIR spectroscopic techniques for rapid identification of strawberry powder made from freshly available and stored strawberry.

Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality

Determining the quality of meat has always been essential for the food industry because consumers prefer superior quality meat. Therefore, the food industry requires the development of a rapid and non-destructive method for meat-quality determination. Over the past few years, a number of techniques have been presented for monitoring meat–chemical attributes. However, most previous techniques are quite expensive, destructive, and require complex hardware to operate. Thus, in this work, we demonstrate a low-cost sensing technique (eliminating the expensive equipment and complicated design) for meat–chemical quality detection. The newly developed system was integrated with a low-cost monochrome camera and ordinary light-emitting diode (LED) light sources, with fifteen different wavebands ranging from 458 to 950 nm. The monochrome camera captures images of the meat sample across a spectral range from 458 to 950 nm using a single snapshot method. The chemical values (e.g., moisture, fat, and protein) were also determined using conventional methods. The collected images were combined to produce a multispectral data cube and to extract spectral data. Partial least squares (PLS) and support vector regression (SVR) modeling were used on the extracted spectra and chemical values. The developed models for meat samples displayed accurate chemical-component prediction ( R 2 > 0.80). Our model, based on a monochrome sensor using only fifteen wavebands, provided reasonable results compared with the previously developed expensive spectroscopic techniques. Therefore, this complementary metal-oxide semiconductor (CMOS) based multispectral sensing technique may have the potential to detect meat quality, thereby facilitating a simple, fast, and cost-effective method applicable to small-scale meat-processing industries.