Heinz W. Siesler

Detecting the peroxide value (PV) in oil is significant for people in everyday life, especially as a fast, convenient, and on-site method. To tackle this challenge, the near-infrared (NIR) spectra of oil were collected by a Viavi MicroNIR 1700 handheld NIR spectrometer and a liquid sample transmission accessory. Subsequently to the spectral pretreatment method of standard normal variate (SNV), the sensitive wavelength variables were optimized by the algorithms of competitive adaptive reweighted sampling (CARS), genetic algorithms (GA), and random frog (RF). The results showed that CARS was the best, and the selected variables were used to build the partial least squares (PLS) regression model. The root mean square error (RMSE) values for cross-validation (RMSECV) and prediction (RMSEP) were 0.984 mmol/ kg and 0.950 mmol/kg, respectively, and the corresponding 

 were 0.875, and 0.867, respectively. Therefore, the PV of edible oil can be determined easily and quickly with a handheld NIR spectrometer.

Because of high-volume manufacturability and the opportunity to significantly reduce costs, numerous companies target handheld near-infrared (NIR) scanners for a non-expert user community for consumer applications. A tremendous impact of handheld NIR spectroscopy for everyday life applications can be expected, ranging from food testing to detection of fraud and adulteration in a broad area of materials (1). The present study focuses on the on-site determination of the peroxide value (PV) in edible oil by handheld NIR spectroscopy.

Edible oil is one of the main raw materials for food, and its PV is one of the important indexes for the evaluation of oil quality. It is an index of the degree of oxidation of oils and fatty acids. During the rancidity process, oil produces some intermediate products that are very unstable and continue to decompose into aldehydes, ketones, and other oxides, which further deteriorates the oil (2). Long-term consumption of food with excessive PV is very disadvantageous to human health because peroxide can destroy cell membrane structure, which could result in gastric cancer, liver cancer, arteriosclerosis, myocardial infarction, hair loss, and weight loss (3). Therefore, a fast detection method for measuring PV in oil is important. Undoubtedly, handheld NIR spectroscopy is the optimal choice because it is fast and easy to use.

For this study, 54 oil samples were collected as the calibration set for the development of the calibration models.

A MicroNIR 1700 instrument combined with a liquid-sample accessory (Figure 1a) was applied for the recording of spectra. For the transflection measurements, a drop of oil was deposited in the trough of the liquid sample accessory, and then the lid was closed (Figures 1a and 1b). First, 50 scans with an integration time of 60 ms were accumulated for the wavelength range 950–1650 nm with a spectral resolution of 12.5 nm. A 99% Spectralon reflection standard (Labsphere Inc.) was used as the reference. The final spectrum was the average of triplicate measurements.

FIGURE 1: Images showing method and apparatus for NIR spectrum collection for oil. (a) A liquid-sample accessory and (b) optical path diagram of spectrum acquisition.

The reference PV of the samples was determined based on the national standard GB5009.277-2016; the unit was mmol/kg.

NIR spectra frequently contain background information, drift, and noise. To obtain reliable, accurate, and stable calibration models, it may be necessary to preprocess the spectral data before modeling (4). In this application, the spectra were pretreated by the standard normal variate (SNV) scatter correction.

In NIR spectra, the spectral information is not evenly distributed over the whole wavelength range under investigation. Therefore, the selection of the informative variables is a significant preprocessing step (5). In the present work, three kinds of wavelength selection methods, competitive adaptive reweighted sampling (CARS), random frog (RF), and genetic algorithm (GA), were applied.

 is based on the simple and effective “survival of the fittest” principle to select wavelength variables, and it selects the optimal combination of wavelength variables with larger absolute regression coefficients in PLS regression (6). RF is a novel feature wavelength optimization method applying the framework of reversible jump Markov Chain Monte Carlo (RJM- CMC) (5). It runs a search in the model space through both fixed-dimensional and trans-dimensional moves between different models, and then a pseudo-MCMC chain is computed to calculate the selection probability (SP) for each variable. The wavelength variable is preferred according to the ranking of all variables based on SP. GA is an adaptive search procedure based on the mechanism of genetics and natural selection (7). The fitness of an individual (its ability to adapt to the environment) is calculated; high-quality individuals are retained and low-quality individuals are discarded. New individuals are generated through inheritance and evolved through natural selection. In this way, eventually, the solution to the problem is achieved.

CARS and RF were run by the lib-PLS toolbox (6), and GA was run by the “genpls” toolboxes.

The Unscrambler (version 10.4, Camo Software AS) was used to develop the PLS calibration models, and the number of factors was determined by 20-fold internal cross-validation. Cross-validation was applied to estimate the prediction error by dividing all the samples into 20 random segments, one segment was reserved for validation, and the remaining 19 segments were used for calibration. This process was repeated until all segments were used for validation once, and then the optimal number of factors was obtained (8).

For this part of the study, 27 unknown samples were collected as a test (or prediction) set to externally validate the prediction capability of the calibration models.

Calibration and validation statistics parameters were the R-squared values (

) and the root mean square errors (RMSEs). For the calibration, cross-validation, and prediction sets, the RMSEs for calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP), respectively, were determined. Accordingly, the 

 (9). The RMSEC, RMSECV, and RMSEP were used to evaluate the feasibility and predictive capability of the model The lower the RMSEP and the closer its value to the RMSECV, the stronger the prediction ability and the greater the robustness of the model. The residual predictive deviation (RPD

) defined by the standard deviation/RMSECV of the cross-calibration set was included to estimate how well the calibration model can predict the compositional data. Generally, an RPDCV value greater than three can be considered as very good for prediction purposes (10).

The oil PV values were determined by a national method and the results are summarized in Table I. For the calibration set and the test set, the mean values were 4.25 mmol/kg and 4.17 mmol/kg, respectively. The corresponding ranges were 4.25–12.23 mmol/kg and 4.17–10.86 mmol/kg, and the standard deviations were 2.76 mmol/kg and 2.65 mmol/kg, respectively. The values were almost the same, which demonstrated that the collected samples for the calibration set and test set were reasonable.

The raw NIR spectra of oil are presented in Figure 2a. The most intense peak at 8343 cm

) vibrations, respectively. The absorption bands at 7243 cm

) combination bands (11), respectively, superimposed by the first overtone of the ν(OH) stretching vibration. As shown in Figure 2b, after SNV pretreatment, especially the spectral differences in the 8000–9000 cm

FIGURE 2: The NIR spectra of oil showing (a) raw spectra and (b) spectra pretreated by SNV.

As mentioned in the previous section, CARS, RF, and GA were used to select wavelength variables. The selection results are shown in Figure 3. Finally, 25, 52, and 40 wavelength variables were selected for the calibration.

FIGURE 3: The results of wavelength variable selection by the three different methods: (a) CARS, (b) RF, and (c) GA.

After the wavelength variables were selected by the CARS, RF, and GA methods, they were subjected to PLS modeling. For the three wavelength optimization methods, they all improved the calibration performance. Although they had almost the same RMSECVs of 0.98 mmol/kg, 0.97 mmol/kg, and 0.93 mmol/kg, CARS had the lowest RMSEC of 0.76 mmol/kg, Therefore, CARS proved to be the best variable selection method. Regarding the RPDcvs values of models with selected variables, they were larger than those of models with the full variables and were close to 3, which demonstrated that the models had accurate prediction capability in the whole range of PV. Figure 4a presents the scatter plot of the reference PV compared to the predicted PV for the calibration and cross-validation sets; the proximity to the regression line demonstrates a good calibration performance.

FIGURE 4: The scatter plots of PV reference compared to the predicted values for (a) the calibration cross-validation sets and (b) the test set.

There were 27 samples collected for the validation of the calibration model. The results are summarized in Table II, where CARS has the smallest RMSEP of 0.95 mmol/kg, compared to 1.14 mmol/kg and 1.15 mmol/kg of RF and GA, respectively. The plot of reference compared to the predicted values is shown in Figure 4b, with the points lying close to the regression line. The large 

 of 0.867 proves that the built model has a good performance for the PV prediction of unknown oil samples and the calibration can be used for future applications.

The PV of edible oil can be easily and rapidly determined on-site by handheld NIR spectroscopy. With the further de- velopment and dissemination of this technology, it will prove very helpful for the protection of human health and also be used for the rapid detection of other quality indicators in food.

(2) N. Zhang, Y. Li, S. Wen, Y. Sun, J. Chen, Y. Gao, A. Sagymbek, and X. Yu, 

(3) R.R. Mariana, E. Susanti, L. Hidayati, and R.A. Wahab, 

(5) H.D. Li, Y.Z. Liang, X.X. Long, Y.H. Yun, and Q.S. Xu, 

(7) X. Shao, F. Wang, D. Chen, and Q. Su, 

(8) T. Næs, T. Isaksson, T. Fearn, and T. Davies, 

A User-Friendly Guide to Multivariate Calibration and Classification

 (NIR publications, London, United Kingdom, 2002). DOI: 

(9) S.X. Fan, B.H. Zhang, J.B. Li, W.Q. Huang, and C.P. Wang, 

Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy

 (CRC Press, Boca Raton, FL, 2nd ed., 2012)

 is with the School of Biotechnology at Jiangsu University of Science and Technology, in Jiangsu, China. 

 is with the Department of Physical Chemistry at the University of Duisburg-Essen, in Essen, Germany. Direct correspondence to: 

Articles

A PLS model was built with optimized wavelength variables generated by a competitive adaptive reweighted sampling (CARS) algorithm, enabling the use of handheld NIR spectroscopy to rapidly detect peroxide values in oil.

Rapid Determination of the Peroxide Value of Edible Oil by Handheld NIR Spectroscopy in Combination with Wavelength Variables Selection and PLS Calibration

	This communication attempts to separate the wheat from the chaff regarding the bid of commercially available handheld near-infrared (NIR) spectrometers and tries to answer the question of whether this technique is already mature for applications in a non-expert user community.

	Taking into account its chemical specificity and the simplicity of sample presentation for a broad range of materials, near-infrared (NIR) spectroscopy is by far the most advanced vibrational spectroscopy technique in terms of hardware miniaturization. This development is additionally fueled by market-research companies that predict a considerable growth rate for this segment of instrumentation within the next few years. With the further reduction of costs by high-volume manufacturability, this technique will also come into reach of a new–non-expert–user community and widespread adoption of such spectrometers for in-the-field testing and on-site measurements in everyday-life applications is forecast. However, these optimistic prospects have, unfortunately, also led to negative commercial developments such as the exaggerated claims of some direct-to-consumer companies regarding the performance of their “NIR scanners” with “cloud evaluation of big data.” Apart from making unrealistic promises with reference to, for example, the determination of the type and accuracy of nutritional parameters (1,2), determination of the freshness of fruit or the identification of stains on textiles (3), most of the advertising videos oversimplify the measurement procedure to a “point-and-shoot” action with any deficiencies in sample presentation and sample representativeness being taken care of by the cloud evaluation. These “garbage in–garbage–out” measurements inevitably lead to unfulfilled expectations of the users, and as a consequence of such failures, to a misjudgment of the real potential of NIR spectroscopy.

	Therefore, in this article the instrumental principles and performance parameters of those instruments that are not only available on the market, but whose performance parameters are also easily traceable in specification sheets or have been verified by the scientific community in test measurements, are summarized. Apart from a couple of exemplary applications, the basic requirements for qualitative and quantitative applications are outlined to provide the interested reader some guidelines for selecting the appropriate hardware for a specific analytical problem . Finally, the maturity of handheld NIR spectroscopy for introduction to a new non-expert user community is briefly discussed from the authors’ perspective.	 

	The recent progress in NIR spectrometer miniaturization has taken advantage of new micro-technologies such as micro-electro-mechanical systems (MEMS), micro-opto-electro-mechanical systems (MOEMS), micro-mirror arrays, and linear variable filters (LVFs) and has led to a drastic reduction of spectrometer-size and weight (<100 g). Although some companies and research institutions are working on the implementation of miniaturized NIR systems in mobile phones (4,5), such devices are not yet commercially available. 

	In Figure 1 the state-of-the-art of miniaturized and commercially available NIR spectrometers is illustrated. Only the most important spectroscopic parameters (spectral range, spectral resolution, and signal-to-noise ratio, or S/N) are included in the figure. While the instruments in the top row are based on (a) an LVF with an InGaAs detector array, (b) a micro-mirror array with a single-element InGaAs detector, and (c) a Fabry-Perot interferometer with a single-element InGaAs detector, the spectrometers in the bottom row (d–f) are exclusively based on Michelson interferometers. The values of the S/N for (a), (b), and (d) have been determined for the available wavelength ranges in our laboratory, while the S/N values of the other systems have been taken from the datasheets in the corresponding websites. Additionally, it is noteworthy that the last-mentioned two systems (e) and (f) only became commercially available since the beginning of 2020.	 

	Generally, these spectrometers can be applied to qualitative identification, such as in the context of fighting fraud (1,6,8) or for the quantitative determination of specific components in a broad range of materials ranging from active ingredients in pharmaceutical formulations to nutritional parameters in food (2,7,8). Two examples illustrate the everyday life applicability of the discussed instruments.

	Textiles are extremely important materials for everyday life with a broad range of applications and properties. Given the increasing quality and price consciousness of customers, the availability of a simple tool for a rapid test of a purchased textile would be a significant progress in customer protection. Although textiles manufactured from pure natural materials (silk, cotton, wool) are generally preferred due to their higher wearing comfort, synthetic polymers are often admixed to modify their tribological properties. In Figure 2a, the diffuse-reflection NIR spectra of different cotton–polyester blends are shown. The wide wavelength range and measurement speed (scan time was only 8 s) of the adopted instrument (Figure 1d) make this spectrometer a powerful tool to quickly estimate the blend composition of the textile under inspection. Apart from many differences in the spectra of pure cotton (black spectrum) and pure polyester (green spectrum) specifically the absorption bands of the 2xν(OH) (6200–7200 cm

) overtones of cotton and polyester, respectively, can be used to verify the composition. For public use by non-experts, however, a chemometric partial least squares (PLS) analysis (including spectral pretreatment procedures) had to be embedded in an app to facilitate rapid evaluation of the textile composition.	 

As a result of increasing criminal activities in the pharmaceutical market, another important prospective everyday life application of handheld NIR spectroscopy is the authentication of pharmaceutical formulations. One of the most widespread attempts of deception is by internet sale of tablets to treat erectile dysfunction. The straightforward discrimination of fake tablets from genuine ones with the handheld LVF NIR spectrometer of Figure 1a was shown several years ago (8). Here, the use of one of the three most up-to-date instruments (Figure 1e) for the identification of traditional Chinese medicines shall serve as a case study.	 

Dendrobium huoshanense C.Z. Tang et S.J. Cheng

 is a rare and endangered medicinal plant (orchid) that occurs only in the northern mountain regions of the Yangtze River. Given its excellent therapeutic merits, 

 (huoshan) has been recorded to nourish the stomach, enhance immunity, resist cancer, prolong life, relieve throat inflammation, and improve eyesight. The stems of huoshan are processed into fengdou, shown in Figure 2b, which is very expensive due to its high medicinal value and supply shortage. Therefore, it would be desirable to discriminate authentic fengdou from product based on 

(henan), which looks very similar, as shown in Figure 2b, but has much lower efficacy and correspondingly a much lower (1/10) price. Because the discrimination is not possible by visual inspection only, a nanoFT-NIR spectrometer was used to measure the diffuse-reflection spectra of 28 huoshan and 28 henan fengdou samples by clamping them between the light fiber tip and the base plate, as seen in Figure 2b and averaging 16 scans (0.2 s/scan). For further processing, the raw spectra measured in the wavenumber range 9990–4200 cm

 (Figure 2c) were truncated to the wavenumber range 6005–4980 cm

 and pretreated by a standard normal variate (SNV) algorithm to correct for scattering effects. In the final principal component analysis (PCA) the first three principal components (PCs) contributed 55.9%, 17.2%, and 12.3% variance, respectively, and the corresponding 3D score plot illustrated in Figure 2d proves that the spectra of huoshan and henan cluster in two classes, thereby providing a suitable data basis for the discrimination of the superior huoshan fengdou samples from the henan species. Comparative evaluations with spectra of short-wavelength portions (9985–6054 cm

) did not provide successful discrimination results. Here too, for the non-expert user, a much simpler sample presentation in combination with an easy-to-use evaluation routine had to be developed.	 

	State-of-the-art miniaturized NIR spectrometers provide suitable instrumental tools to solve qualitative as well as quantitative analytical problems by on-site and in-the-field measurements with a sufficient performance level. In a multiplicity of qualitative and quantitative investigations with the purpose to rank different handheld NIR spectrometers (6,7,9) the following performance features have been derived: First, for qualitative tasks including authentication, discrimination, and identification, the availability of a broad wavelength range in general and the long-wavelength range specifically had priority over S/N. Second, irrespective of a narrower wavelength range, instruments with a high S/N ratio yielded better calibration parameters in the quantitative multivariate analysis compared to instruments with a larger spectral range but inferior S/N. 

	However, in spite of suggestions by several direct-to-consumer companies in advertisements and videos, the technique has not yet reached the necessary maturity for operation by non-experts, not to mention data interpretation. On the one hand, to achieve these goals, clear guidelines regarding sample presentation and sample-dependent settings of measurement parameters (such as to compensate for sample heterogeneity) have to be provided by the vendors. On the other hand, the spectra pretreatment and evaluation algorithms have to be embedded in interactive apps with simple yes/caution/no (“traffic light”) answers for qualitative identification or discrimination problems or component-specific concentration specifications for quantitative investigations. 

	Nevertheless, we believe that in the near future these miniature handheld NIR spectrometers will not only enable non-expert users in new measurement environments to gain important benefits, but that these spectrometers-stand-alone or integrated into mobile phones-and equipped with elaborate operation and evaluation software, will be broadly adopted by the public.	 

	The authors gratefully acknowledge the measurement of the fengdou spectra by SouthNest Technologies Co, Ltd., Hefei, China.	 

	1.     N. O’Brien, C.A. Hulse, F. Pfeifer, and H.W. Siesler, 

	2.     M.D. Neves, R.J. Poppi, and H.W. Siesler, 

	4.     T. Pügner, J. Knobbe, and H. Grüger, 

	5.     https://trinamix.de/nir-spectroscopy/mobile-spectroscopy-solution; accessed 04/01/2020.

	8.     M. Alcalà, M. Blanco, D. Moyano, N. Broad, N. O’Brien, D. Friedrich, F. Pfeifer, and H.W. Siesler, 

with the School of Biotechnology at the Jiangsu University of Science and Technology in Zhenjiang, China. 

 is a professor with the College of Biological and Pharmaceutical Engineering at West Anhui University, in Lu’an, China. 

 is a professor with the Department of Chemistry at the University of Duisburg-Essen in Essen, Germany. Direct correspondence to: 

In celebration of Spectroscopy’s 35th Anniversary, leading experts discuss important issues and challenges in analytical spectroscopy.

Handheld Near-Infrared Spectrometers: Reality and Empty Promises

Heinz W. Siesler, Emeritus Professor at University of Duisburg-Essen, reviews Jerry Workman’s new book, 

The Concise Handbook of Analytical Spectroscopy Theory, Applications, and Reference Materials

	Getting straight to the point: This handbook will not only become a standard work on the shelves of university and industrial libraries, but it will also become a routine reference source of knowledge on the desks of scientists involved in analytical spectroscopy.

	The handbook consists of five volumes that provide an extremely detailed overview of the theoretical principles, state-of-the-art instrumentation and its operation, data acquisition, data evaluation, and practical applications of electronic and vibrational spectroscopy (ultraviolet, visible, near-infrared, mid-infrared, and Raman spectroscopy). Because the individual volumes are also intended as stand-alone books for analysts working with the specific technique, certain parts of the text (for example, theoretical introduction and spectra evaluation techniques) are inevitably repetitions; however, this structure provides the benefit that the necessity for referral to the other volumes is kept to a minimum.

	Generally, the outstanding advantage of this handbook is the wealth of information provided in addition to the key topics regarding the described technique. Whether it is historic developments, important literature references, band assignments, modern evaluation algorithms, characteristic curves, or specific reference materials, everything is compactly described in each technique-specific volume.

	Compared to other books on the same topics, this handbook is also brilliant when it comes to the clearness and detail of the figures, which illustrate the function of the instruments and accessories described in the text. Referring to the proverb “a picture is worth a thousand words,” this feature will be an additional attraction of the handbook for students and scientists who are not necessarily experts in electronic and vibrational spectroscopy.

	For practitioners in analytical spectroscopy, the different volumes of the handbook provide a broad range of application examples of the chemical and physical analysis of solid, liquid, and gaseous materials. Sample preparation and possible artifacts originating thereof are discussed in detail, and qualitative and quantitative analysis is treated in terms of univariate as well as multivariate chemometric evaluation procedures.

	Of special note is the practical value and importance of the reference material spectra and tables for the individual techniques. Furthermore, real-life applications from the chemical, pharmaceutical, cosmetic, polymer, and food industries as well as environmental investigations illustrate the relevance of the specific techniques for research and quality-process control and facilitate their implementation as industrial routine tools. The books also enable readers to assess the pros and cons of the described spectroscopic techniques relative to other analytical tools.

	Because this set of books endeavors to focus on high-quality laboratory measurement techniques, the topic of instrument miniaturization was not discussed.

	In summary, I would like to congratulate Jerry Workman for his enthusiasm and perseverance to provide the scientific community with such an extraordinary oeuvre that will remain the standard source of specific knowledge in electronic and vibrational spectroscopy for a long time. It will likewise gain recognition by specialists and satisfy the curiosity of newcomers for spectroscopy. Applied as a reference source in analytical science education, this handbook will most importantly attract the future interests of students for this field of research.

 is Emeritus Professor in physical chemistry at the University of Duisburg-Essen, in Germany.

Heinz W. Siesler, Emeritus Professor at University of Duisburg-Essen, reviews Jerry Workman’s new book, The Concise Handbook of Analytical Spectroscopy Theory, Applications, and Reference Materials

Heinz W. Siesler

Articles

Rapid Determination of the Peroxide Value of Edible Oil by Handheld NIR Spectroscopy in Combination with Wavelength Variables Selection and PLS Calibration

Handheld Near-Infrared Spectrometers: Reality and Empty Promises

Book Review: The Concise Handbook of Analytical Spectroscopy Theory, Applications, and Reference Materials