1 Application of NIRS to Olives, Olive Oil, and Cheese Products Emil W. Ciurczak Castelo-Branco, Portugal 28 May 2015
2 Why Near-Infrared? Used first by US Dept. Agriculture in 1960s for wheat, soy, corn, fruits; later cotton, milk, tobacco, etc. Can be used for as is plants and fruits a well as processed materials, such as cheese and milk Can be used in the field as well as laboratory Minimum sample preparation, no chemicals or dilutions Once calibrated, may be used by anyone in any situation Cost of equipment and training is quickly offset by major time savings
3 Determination of Chlorophyll in Olive Oil There are various grades of olive oil; extra virgin regular light Extra Virgin olive oil is considered the highest quality, the first pressing from freshly prepared olives. (It has a greenish-yellow tint and a distinctively fruity aroma because of the high levels of chlorophyll and other volatile materials extracted from the fruit. Regular olive oil is collected with the help of a warm water slurry to increase yield. (It is pale yellow in color, with a slight aroma, because it contains fewer volatile compounds) Light olive oil is very light in color and has no aroma because it has been processed under pressure to remove the chlorophyll and volatile compounds. (It is commonly used for frying because it does not affect the taste of fried foods and it is relatively inexpensive).
4 Non-destructive assessment of olive fruit ripening by a portable NIR A portable NIR was used for the determination of oil and moisture contents in intact olives. In this test, spectral data were recorded in the region from 1100 to 2300 nm at 1 nm intervals under two different experimental conditions: on-tree in the field in Trial 1 under laboratory room conditions in Trial 2. Calibration models were developed and evaluated using PLS regression separately for each trial set and for the combined group of samples.
5 Non-destructive assessment of olive fruit ripening by a portable NIR The combined model showed predictive statistics within the range of the individual models (R = 0.89 and RMSECV = 1.99 for oil content and R = 0.88 and RMSECV = 2.06 for moisture content), considered acceptable as an increase in the model robustness could be expected. These results encourage the use of portable NIR spectroscopy to monitor olive fruit ripening and to decide the optimal harvesting date on the basis of oil and moisture content. Although slightly better results were obtained under laboratory room conditions, the results obtained on-tree in the field were also accurate enough to determine the optimal harvest date of each cultivar.
6 Experimental Method In trial 1, NIR spectra of fruits on-tree were obtained, samples were then taken to laboratory. In trial 2, fruits were harvested, and brought to laboratory spectra collected. The average spectra of five fruits per date were used for later analyses in both trials. Spectra were acquired in absorbance with a portable spectrophotometer, between 1100 to 2300 nm at 1 nm intervals. Each spectrum is the average of 50 spectra acquired on the fruit equator with continuous measuring for a total scanning time of 5 sec. Spectra collection was controlled with a laptop computer (SNAP32 software)
7 Methodology After spectral collection, fruit samples were processed in the laboratory for analysis by reference methods. Fresh samples were weighed and then dried in an oven (105 C for 42h) to determine moisture content. The oil content of dried samples was recorded by NMR Minispec NMS100 (Bruker). Calibration models were developed separately for each trial set and for the combined group of samples. Full cross-validation (i.e. leaving-one-out) was used to determine the performance of the models and no outliers were removed in any step of the calibration process. Correlation between actual and predicted constituent values (r) and standard error of cross validation (RMSECV) were used to test the performance of calibrations (Shenk and Westerhaus, 1995).
8 Results and Discussion Reference data and Spectral Features Environmental conditions and ripening stages have provided wide ranges of variability for the characteristics evaluated (Table 1). For the combined group oil content ranged from 4.80 to % and moisture content from to %. In both trials 1 and 2 oil content increases at the beginning of the experiment and then stabilizes or even slightly decreases at the end of the ripening period (Figure 1). Moisture content showed the opposite trend decreasing during the ripening period; both oil and moisture content were highly correlated (R = 0.61, p < 0.001). Different patterns were observed in trials 1 and 2 probably due to the different climatic conditions of each experimental area.
9 The average raw spectra and coefficient of variation of olive fruit samples from T 1 and T 2 are shown in Figure 2: two water bands ~1460 nm & 1950 nm and oil around 1210 & 1730 nm. Calibration development for each trial set Calibration models were developed for moisture and oil contents for each trial independently and for the combined data set. The number of PLS factors, correlation coefficient and predictive error (RMSECV and RER) obtained for models are shown in Table 2. R values for oil content and moisture were slightly lower and RMSECV was higher for model 1, due to different conditions during data acquisition. On-tree fruit spectral data were used in model 1, while the spectral data of model 2 were obtained under constant laboratory room conditions.
10 Results and Discussion Reference data and spectral features The selection of different cultivars, environmental conditions, and ripening stages have provided wide ranges of variability for the characteristics evaluated (Table 1). In both trials 1 and 2 oil content increases at the beginning of the experiment and then stabilizes or even slightly decreases at the end of the ripening period (Figure 1). Moisture content showed the opposite trend decreasing during the ripening period. Oil and moisture content, were highly correlated (r= 0.61, p < 0.001). Different patterns were observed in trials 1 and 2, probably due to the different climatic conditions of each experimental area.
11 The average raw spectra and COV of samples from T 1 and T 2 are seen in Figure 2. Spectra are characterized by two water bands ~1460 nm and 1950 nm and oil ~1210 nm and 1730 nm. Calibration development for each trial set Calibration models were developed for moisture and oil contents for each trial independently and for the combined data set. The number of PLS factors, correlation coefficient and predictive error obtained for models are shown in Table 2. R values for oil content and moisture were slightly lower and RMSECV was higher for model 1 (probably due to different conditions during spectral data acquisition)
12 On-tree data were used in model 1; data for 2 obtained in the laboratory The NIR prediction of fruit moisture and oil content in intact olives has been previously reported. León et al. (2003, 2004) obtained calibration models accurate enough to predict oil content and moisture with r values of 0.94 and 0.93 Cayuela et al. (2009) obtained variable predictive ability based on the sample presentation reference laboratory method. The best results provided r values of 0.83 and 0.88 and RER values of 7.8 and 11.8 for oil content and moisture. Using a different portable instrument, Cayuela and Pérez- Camino (2010) obtained r values of 0.78 and 0.76 and RER values of 10.6 and 10.3 for oil content and moisture respectively.
13 The different cultivars evaluated in this work showed differences in lipid synthesis both in total amount of oil formed and the period of time, as well as the evolution of fruit moisture during ripening (Figures 3 and 4). The prediction values for oil content and moisture by cultivar and sampling date were closely correlated with reference
14 Figure 1. Change in moisture and oil content during the ripening period. Each point represents the mean value of 24 samples (Trial 1) and 16 samples (Trial 2); error bars indicate the SE
15 Figure 2: Avg. Spectra and CoV of Olive fruit samples Trial 1 = Black; Trial 2 = Grey
16 Tables 1 and 2
17 Figure 3: Reference values v. Predicted values
18 Figure 4
19 Comparison of NIR and lab for oil and moisture
20 Rapid, reliable analysis can contribute to process and quality improvements in numerous ways. For example, Assessment of raw olive acceptability. If the olives have been collected from the ground rather than fresh from the tree, they may be of poor quality with high acidity and hence lower value. Measurement of water and oil content. These parameters determine the price of the olives, with those having a greater oil content commanding a higher price. Process optimization. After extracting the oil, the remaining pulp or by-product (called alperujo) should have only minimal oil content, typically around 2% or less. If the oil exceeds this level, a problem with the process is indicated.
21 Methodology Olive samples were milled to a paste and placed in a glass petri dish before analysis. Spectra were collected between and 4000 cm -1 at 16 cm -1 resolution, with an accumulation time of 30 seconds per sample. The olive samples were also analyzed for oil and water content following the customer s established laboratory procedures. Some of the measured spectra are shown in Figure 1. Typically for NIR spectra, the absorption features are broad and overlapped, although several prominent features can be assigned either to water or to organic C H modes in the oil.
22 Figure 1: A number of olive samples CH = oil bands
23 Building the Software Model Using Principal Components 1. Define materials and acquire spectra of known references. 2. Optionally, configure algorithm parameters and spectral preprocessing such as baseline correction 3. Calibrate the method. The software automatically builds the models and determines the acceptance thresholds. 4. Review the classification results (for example, see Figure 2). Any issues with the data or performance of the method will be flagged by the troubleshooting engine, allowing corrective action to be taken. 5. The validated method is then deployed as a workflow within the dedicated Analyzer module, allowing routine use of the method.
24 Quantitative Modeling of Oil and Water The oil and water content of the olives are key parameters for quality and both contribute to the NIR spectrum. The complex nature of NIR spectra sometimes makes it impossible to develop quantitative models based on the absorbance at a single wavelength. However, multivariate (Chemometric) methods such as partial least squares regression (PLS) still function in the presence of overlapping bands, and will allow models to be built. The olive spectra and properties determined by chemical analysis were loaded into software. One third of the data were designated as a validation set to verify the performance of the model. The spectra were pre-processed with first-derivative baseline correction.
25 Table 1: Properties of the olives for water and oil The calibration and validation results are summarized in Table 1 and Figure 3. The models use a modest number of latent variables and show good linearity and precision over the range of available samples. The standard errors of prediction (SEPs) were 1.5 % and 1.7 % for oil and water, respectively.
26 Experimental Results # PCs v. SEP Oil Water PLS Calibration PLS Validation
27 Analysis Scheme
28 What can be analyzed by NIR?
29 Techniques used for each assay
30 Results of Analyses Olive Leaves Soils Olive fruit (intact) Olive Paste
31 Instrument Specs Courtesy Unity Labs
32 Olive Oil Spectra
33 Calibrations for Fat and Moisture Fat N = number of samples RSQ = Correlation Coefficient (NIR vs. wet chem) Min = minimum reference value SECV = Cross Validation Error Moisture
34 Laboratory Measurements Courtesy Bruker Instruments
35 Spectra of EVOO, Soybean oil, EVOO adulterated w/50% soy, pure all natural OO
36 FT-NIR Analysis of Edible Oil Quality FT-NIR spectroscopy can help to: Identify the incoming oil Assess the quality of the oil Evaluate the frying capabilities Analysis: No sample preparation filling of 8mm disposable vials Temperature control at 50 C Measurement time: approx. 20 sec
37 Typical Performance for NIR Analysis to Check the Quality of Fresh Edible Oils Property Data Set Prediction Error Name Unit n Min Max RMSEP C16:0 Palmitic Acid % C18:0 Stearic Acid % C18:1 Oleic Acid % C18:2 Linoleic Acid % C18:3 Linolenic Acid %
38 Typical Performance for NIR Analysis to Check the Quality of Fresh Edible Oils Property Data Set Prediction Error Name Unit n Min Max RMSEP Gardner Color TFA Trans Fatty Acids % TFA low range % IV Iodine Value FFA Acidity %
39 FT-NIR Analysis of oils & fats in transflection Calibration development in progress Calibration from room temperature to 50 o C Lower but still sufficient accuracies Probably less possible components or parameters to be calibrated
40 Hand-Held Models are Available microphazir RX Handheld NIR Material Analyzer (a) and QualitySpec Trek ( nm) Hand-held Spectrometer (b) a b Courtesy Ahura [a] (Thermo) and ASDI [b]
41 Area NIR using Drones Whole fields or groves can be scanned
42 LandSat Images showing Productivity
43 And then there s dairy products
44 QC of Cheese by NIR Fat /- 0.3% Dry matter /- 0.5% Fat Dry Matter
49 Dry Matter and Fat in Cheese Dry Matter Fat Cheese Standards
50 Typical Instrument Spec Sheet
51 Conclusions Because of its versatility (many types of sample presentations), NIR is quite useful in food and agriculture With over 60 years of experience, there are many, many references for food applications available, either in a library or through instrument manufacturers Instruments range from sophisticated laboratory to rugged hand-held or instrument/vehicle mounted monitors The speed of analysis allows for may more samples to be (non-destructively) measured, assuring a good crosssample of a field or grove
/1 e Branch Pharmaceutical Keywords Near-infrared spectroscopy, lyophilized pharmaceuticals, water determination, Karl Fischer titration, loss on drying (LOD) Summary This Application Bulletin describes
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