The performance of a single instrumentation platform, incorporating the use of a tungsten halogen light source, body transmittance optics and a silicon photodiode array detector, and a uniform chemometric approach is reported for the application of assessment of determination of soluble solids and dry matter content of a range of fruit. Spectra were acquired at integration times of 30 ms or less, with integration time varied between fruit types to achieve a similar signal level. Calibration performance was compared in terms of root mean standard error of cross validation (RMSECV), regression coefficient (R), and the SDR (SDR = SD / RMSECV (SD is standard deviation)]. The technology was well suited to sorting on soluble solids content (SSC) in apple (RMSECV 0.22%, SDR > 5; R 0.98), and useful, in decreasing order of accuracy, for sorting of stonefruit, mandarin, banana, melons, onions, tomato and papaya (RMSECV 1.1%, SDR 1.6, R 0.79). The technology also performed well in sorting on dry matter content in kiwifruit (RMSECV 0.38%, SDR > 3, R 0.95), and useful, in decreasing order of accuracy, for sorting of banana, mango, avocado, tomato and potato (RMSECV 1.0%, SDR 1.7, R 0.79). The limitations of the application of the technology to fruit sorting is discussed in terms of fruit type ("skin" thickness) and population range. For example, calibration RMSECV was only 0.20% on tomato SSC, but as population variation was low (SD 0.30%), a poor R (0.77) and SDR (1.5) was obtained.

In near-infrared (NIR) spectroscopy, the transfer of predictive models between Fourier transform near-infrared (FT-NIR) and scanning–grating-based instruments has been accomplished on relatively dry samples (< 10% water) using various chemometric techniques—for example, slope and bias correction (SBC), direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT), and application of neural networks. In this study, seven well-known techniques [SBC, DS, PDS, double-window PDS (DWPDS), OSC, FIR, and WT], a photometric response correction and wavelength interpolative method, and a model updating method were assessed in terms of root mean square error of prediction (RMSEP) (using Fearn's significance testing) for calibration transfer (standardization) between pairs of spectrometers from a group of four spectrometers for noninvasive prediction of soluble solid content (SSC) of melon fruit. The spectrometers were diffraction grating-based instruments incorporating photodiode array photodetectors (MMS1, Carl Zeiss, Jena, Germany), used with a standard optical geometry of sample, light source, and spectrometer. A modified WT method performed significantly better than all other standardization methods and on a par with model updating.

Near infrared spectroscopy can be employed in the non-invasive assessment of intact fruit for eating quality attributes such as soluble solid content (SSC). Rapid sorting is dependent on a suitable non-contact geometry of fruit, light source and detector assembly, optimized for a given fruit commodity. An optical system was designed with reference to distribution of SSC and light penetration into rockmelon fruit. SSC of mesocarp tissue was not significantly different over the greater part of the proximal-distal axis of the fruit, particularly in the vicinity of the fruit equator. There was also no consistent variation in SSC of mesocarp tissue with respect to radial position of sampling. Mesocarp SSC was higher (~3% w/v) closer to the seed cavity. The optical sampling system was therefore designed to assess an equatorial position on the fruit. Light penetrating a rockmelon fruit was empirically assessed to be diffuse at a depth of <15 mm from the fruit surface. Signal decreased in an exponential proportionality with depth into the fruit, but was still detectable at depths in excess of the seed cavity of rockmelons. A partial transmittance optical design was employed, with a collimated light source interrupted by a central light stop, and a detector viewing the shadowed region of the sample. This system did not physically contact the sample. It was compared to a system with a light excluding `contacting' shroud between the detector and the fruit surface. The performance of calibrations generated using the non-contact configuration was not significantly different than for the configuration requiring contact.

Instrumentation for near-infrared (NIR) spectroscopic applications should be optimized for the intended application. The influence of signal precision and wavelength resolution was considered for the application of the noninvasive assessment [NIR 700–1050 nm, partial least-squares (PLS) calibration] of the sugar content of fruit, with the use of a model system of sucrose solution on cellulose. The precision (as estimated at the maximum count of the spectrum) of an MMSI Zeiss spectrometer (Carl Zeiss Pty. Ltd.) was varied by altering the number of scans averaged per spectrum, as well as the signal level. Wavelength resolution was varied between 8 and 20 nm (as estimated of the 912 nm Ar peak) by adjustment of the entrance slit of a prototype spectrometer employing a Hamamatsu S4874Q photodiode array as the detector, constructed on an optical bench. PLS calibrations were developed from interactance spectra of 0–20% w/v sucrose solution soaked filter papers, and compared on the basis of standard error of cross-validation (SECV) and coefficient of correlation-validation ( Rv). The optimum measurement precision for calibration development was lower than expected at a coefficient of variation (CV) of 0.02 [signal-to-noise ratio (SNR) 5000:1]. Calibration performance was poorer at a resolution of < 8 nm full width at half-maximum (FWHM) in the NIR region, but not significantly different at resolutions of between 8 and 20 nm. Further work is required to define the upper threshold of wavelength resolution. We conclude that instrumentation for the application of fruit sorting can have a relatively poor resolution, and can afford to operate at signal-to-noise levels considered low for a photodiode array detector.

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).

Abstract. Spectral data were collected of intact and ground kernels using 3 instruments (using Si-PbS, Si, and InGaAs detectors), operating over different areas of the spectrum (between 400 and 2500 nm) and employing transmittance, interactance, and reflectance sample presentation strategies. Kernels were assessed on the basis of oil and water content, and with respect to the defect categories of insect damage, rancidity, discoloration, mould growth, germination, and decomposition. Predictive model performance statistics for oil content models were acceptable on all instruments (R 2 >0.98; RMSECV <2.5%, which is similar to reference analysis error), although that for the instrument employing reflectance optics was inferior to models developed for the instruments employing transmission optics. The spectral positions for calibration coefficients were consistent with absorbance due to the third overtones of CH 2 stretching. Calibration models for moisture content in ground samples were acceptable on all instruments (R 2 >0.97; RMSECV <0.2%), whereas calibration models for intact kernels were relatively poor. Calibration coefficients were more highly weighted around 1360, 740, and 840 nm, consistent with absorbance due to overtones of O-H stretching and combination. Intact kernels with brown centres or rancidity could be discriminated from each other and from sound kernels using principal component analysis. Part kernels affected by insect damage, discoloration, mould growth, germination, and decomposition could be discriminated from sound kernels. However, discrimination among these defect categories was not distinct and could not be validated on an independent set.It is concluded that there is good potential for a low cost Si photodiode array instrument to be employed to identify some quality defects of intact macadamia kernels and to quantify oil and moisture content of kernels in the process laboratory and for oil content in-line. Further work is required to examine the robustness of predictive models across different populations, including growing districts, cultivars, and times of harvest.A R 0 3 1 7 9 A s s e s s i n g m a c a d a m i a d e f e c t s u s i n g N I R s p e c t r o s c o p y J . G u t h r i e e t a l .

Sugar "imaging" of fruit has previously been reported using NIR fi lters and relatively expensive (high signal-to-noise) charge-coupled device (CCD) instrumentation. In a bid to use lower cost CCD instrumentation (criterion of less than AU $5,000 total component costs), the signal-to-noise constraint on calibration model performance was investigated by artifi cially degrading spectra from a 15-bit AtoD system. A low cost 8-bit CCD camera was then used in conjunction with a fi lter wheel in a transmittance confi guration employing three 50 W halogen lamps. Multiple linear regregression calibrations were developed based on absorbance data of fi ve wavelengths (830, 850, 870, 905 and 930 nm) relevant to sugar and water. Calibration models for the sucrose concentration of solutions on a cellulose matrix were poor (R 2 = 0.4) when based on individual pixel data, but acceptable (R 2 = 0.98, RMSECV = 1.1) (n = 20, mean = 13.9% total soluble sugars (TSS), SD = 6.04) when based on an average of a 23 × 23 pixel block (i.e. 529 pixels). For a calibration based on melon tissue TSS, using spectral data averaged over groups of 529 pixels, results were poorer than expected (R 2 = 0.4, RMSEP = 1.74 (n = 163, mean = 9.45, SD = 2.07% TSS). Predicted TSS output for all pixel blocks from an image was used to generate a false colour image. We conclude that this application requires a higher level of signal-to-noise (for example, 10-bit, > 60 dB CCD).

The use of a model developed on spectra of one (master) instrument with spectra collected using another (slave) instrument requires differences in spectra of master and slave units to be orthogonal to the calibration model. The more spectral similarity is achieved in hardware, i.e. by matching the optical characteristics of the devices, the less chemometric correction is required. The transfer of partial least squares models for total soluble solids (TSS) of intact apple fruit between instrumentation based on silicon photodiode arrays was improved by use of more accurate wavelength assignments over the wavelength range used in the model. Several transfer methodologies were trialled, including piecewise direct standardisation (PDS), transfer by orthogonal projection, model updating (MU) and difference spectrum adjustment. The difference spectrum method combined with new wavelength assignments and MU gave results comparable to the performance of the master instrument and to models directly developed on the slave instruments (r 2 = 0.95, SEP -b = 0.47 and bias = −0.03% TSS, for a population of mean = 14.45 and SD = 1.64% w/v). The use of average difference spectrum adjustment combined with MU was preferred over PDS because of ease of implementation.